Methods and systems for growing and retaining the value of brand drugs by computer predictive model

ABSTRACT

The present invention is directed to a brand value growth and retention system for brand drugs commercialized by brand drug advertisers through a brand drug&#39;s lifecycle during patent exclusivity and after loss of exclusivity. The brand value growth and retention system iteratively analyzes combined computational models of consumer, healthcare provider retailer and payor segment data to produce brand drug promotional campaigns that are predictive with modifying parameters that transform the promotional campaigns over time. As a result, the brand drug promotional campaign generates an increased number of brand drug purchases while predicting the point where incremental promotional campaign investments produce a diminishing number of incremental brand drug purchases.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. Nonprovisional applicationSer. No. 14/214,636, filed on 14 Mar. 2014, which claims priority toU.S. Provisional Application Ser. No. 61/801,978 entitled “Methods andSystems for Growing and Retaining the Value of Brand Drugs by ComputerPredictive Modeling,” filed on 15 Mar. 2013, the disclosures of whichare incorporated herein by reference in their entireties.

BACKGROUND Technical Field

The present invention relates generally to process and optimizationsoftware, and more particularly to computer predictive models ofconsumer, healthcare provider, retailer, pharmacy and payor segment datato generate a flexible, responsive and adaptive promotional campaign forgrowing and retaining the value of brand drugs during a drug's lifecycleincluding its launch phase, growth phase as well as the phase around thetime of loss of exclusivity (LOE).

Related Art

Brand drugs marketed by most brand drug advertisers provide the basisupon which many of these companies are able to meet consumer medicalneeds and generate revenue and profits during a brand drug's lifecycle.The brand drug lifecycle also overlaps with the period of marketexclusivity defined by the brand drug's patents. This period of marketexclusivity provides years of market sales monopoly for the brand, but,at the same time, it imposes a limited duration of revenue and profitdue to the expiration of the associated patents. As brand drug patentsexpire, brand drug advertisers confront the inevitable risk of rapid andsignificant loss of revenue and profits.

Each year, billions of dollars of brand drugs lose exclusivity therebyopening the way for generic manufacturers to enter the market with thesame or similar drug at greatly discounted prices. It is estimated thatin the United States $267 billion of brand drugs will lose patentexclusivity from 2010 to the end of 2016, and more than $50 of billionbrands will become generic within the following five years. A branddrug's sales during its lifecycle often reach peak at the time of LOE;such was the case for Singulair. The annual sales of Singulair at thetime of loss of exclusivity were approximately $3.3 billion, making itthe biggest selling prescription drug for its manufacturer, Merck. Afterlosing its patent exclusivity Singulair suffered a precipitous andmaterial decline in revenue and profit with sales dropping 90% in justfour weeks. It is estimated that brand drugs typically retainsignificantly less than 10% brand share when reaching the period ofpost-exclusivity.

Brand drug advertisers have long been known to invest billions ofdollars to bolster the sales of their brand drugs. Second to drugresearch and development costs, the combined costs of sales, marketingand promotion far exceed any other single expense item for most Branddrug advertisers. A major and recurring challenge for brand drugadvertisers is the lack of predictive methods that can be applied to thecombined individual promotional investment decisions for brand drugs todrive the highest sales of brand drugs while at the same time predictingthe point at which massive investments in promotion no longer produceincremental value. There is a need for a real-time, predictive systemthat combines the correlations of various consumer, healthcare provider,retailer and payor segment data for predictive value. The benefits ofsuch a system for brand drug advertisers are greater brand sales atsignificantly lower costs. With the growing pressure to containhealthcare system costs, methods such as the brand drug value growth andretention system as described below can have a meaningful and materialimpact on the industry.

Given the finite time that brand drug advertisers' patents allow for theexclusive sale of the related brands, it is desirable to grow and retainbrand drug value during the brand's lifecycle, which includes its launchphase, growth phase and the phase around the loss of exclusivity phase.It is therefore desirable to have a software predictive model togenerate, apply, refine, modify, transform, and improve, with a feedbackmechanism through machine learning, one or more promotional campaignsfor growing and retaining brand drug value.

SUMMARY

Methods, computer program products, and computer systems are describedfor growing and retaining the value of brand drugs by predictivecomputational modeling of consumer segment, healthcare provider segment,retailer segment, and payor segment data to generate a promotionalcampaign during a brand drug launch phase, growth phase, or around theloss of exclusivity phase. A brand drug value growth and retentionengine comprises a financial model simulator module, a consumer segmentsmodule, a healthcare segments module, a retailer segments module, apromotional campaign module, a manufacturer copay card pricing module, amanufacturer brand execution module, and other modules. The consumersegments module is configured to provide a computational modeling ofconsumer segments to determine an optimal promotional plan for adirected consumer segment for a brand drug. The healthcare providermodule is configured to provide a computational modeling on healthcareprovider segments for the brand drug. The manufacturer PBM/payorstrategy module and the manufacturer PBM/payor execution module areconfigured to provide a computational model of payor segments. Thefinancial model simulator module is configured to receive thecomputational model consumer segment data, the computational modelhealthcare provider segment data, and the computational model payorsegment data, and executes a predictive model of promotional tactics tosegments of the consumers, healthcare providers and payors to produce anoptimal promotional campaign for the specified brand drug.

A promotional campaign represents a combination of segment promotionalplans. A first set of segment promotional plans is for rolling out toconsumer segments, where each segment promotional plan has one or moretactic profiles. A tactic profile is applicable when a particularconsumer segment responds well to the directed promotional tactic. Asecond set of segment promotional plans rolls out to healthcare providersegments, where each segment promotional plan has one or more tacticprofiles. A tactic profile is applicable when a particular healthcareprovider segment, which can be grouped by behavior of individualhealthcare providers, responds well to the directed promotional tactic.A third set of segment promotional plans rolls out to payor segments,where each segment promotional plan has one or more tactic profiles. Atactic profile is applicable when a particular healthcare providersegment, which can be grouped by behavior of individual healthcareproviders, responds well to the directed promotional tactic.

The brand drug value growth and retention engine includes a dashboardinterface module configured to provide a user interface to communicatedata and control information between the brand drug value growth andretention engine and a master dashboard located at the computer deviceof the brand strategist. The dashboard is partitioned into differentsections for displaying the consumer segment data, healthcare providerdata and payor segment data, as well as the predictive modeling resultof a recommended promotional campaign. Alternative segment promotionalplans are also provided when the current promotional campaign is lessthan optimal as determined by the financial model simulator module.

Broadly stated, a method for selecting a promotional campaign in thehealthcare industry, comprising executing a first computational model onthe consumer segments data to determine a first substantially optimalbrand drug promotional mix for consumers who are candidates for abrand-name drug; executing a second computational model on healthcareprovider segments data to determine a second substantially optimal branddrug promotional mix for healthcare providers who treat the consumerswho are candidates for the brand-name drug; executing a thirdcomputational model on payor segment data to determine a substantiallyoptimal contracting strategy for the brand-name drug; and generating apromotional campaign for the brand-name drug by running a predictivemodel of the consumer segments data, healthcare provider segments dataand payor segment data, based on the combination of outputs from thefirst, second and third computational models.

Advantageously, the present invention is an effective predictive modelfor generating and optimizing a promotional campaign for brand drugadvertisers to grow and retain the value of brand drugs when launchingor growing a product, or around the time of patent expiration.

The structures and methods of the present invention are disclosed in thedetailed description below. This summary does not purport to define theinvention. The invention is defined by the claims. These and otherembodiments, features, aspects, and advantages of the invention willbecome better understood with regard to the following description,appended claims and accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be described with respect to one or more variousembodiments thereof, and reference will be made to the drawings. Thedrawings are provided for purposes of illustration and merely depicttypical or example embodiments of the invention. These drawings areprovided to facilitate the reader's understanding of the invention andshall not be considered limiting of the breadth, scope, or applicabilityof the invention. It should be noted that for clarity and ease ofillustration these drawings are not necessarily made to scale.

FIG. 1 is a high-level block diagram illustrating a brand drug valuegrowth and retention system in a cloud computing environment inaccordance with the present invention.

FIG. 2 is a block diagram illustrating one embodiment of a brand drugvalue growth and retention engine in accordance with the presentinvention.

FIG. 3 is a pictorial representation of a brand drug lifecycle withseven key strategies in accordance with the present invention.

FIG. 4A is a flow diagram illustrating a predictive model of brand drugvalue growth and retention in pre-LOE and post-LOE phases in accordancewith the present invention; and FIG. 4B is a flow diagram illustrating apredictive model of brand drug value growth and retention in the launchphase of a brand drug in accordance with the present invention

FIG. 5 is a flow diagram illustrating a first embodiment of a predictivemodel method in brand drug value growth and retention in accordance withthe present invention.

FIG. 6A is a table illustrating a promotional campaign from a collectionof segment promotional plans in accordance with the present invention;FIG. 6B is a graphical curve illustrating different promotional tacticsin accordance with the present invention; FIG. 6C is a flow diagramproviding one illustration of promotional campaign at time t₀ withmultiple promotional tactics that are applied to multiple segments ofconsumers; FIG. 6D is a flow diagram providing one illustration ofpromotional campaign during a first time period with multiplepromotional tactics that are applied to multiple segments of consumers;and FIG. 6E is a flow diagram providing one illustration of promotionalcampaign during the first time period and a second time period withmultiple promotional tactics that are applied to multiple segments ofconsumers.

FIG. 7 is a flow diagram illustrating a second embodiment of apredictive model method in brand drug value growth and retention inaccordance with the present invention.

FIG. 8 is a graphical curve illustrating the impact of the incrementalvalue to the brand drugs by the promotional campaign launched by thebrand strategist relative to a conventional approach.

FIG. 9A is a flow diagram illustrating the communications between thebrand strategist dashboard, the drug manufacture dashboard and the payordashboard in accordance with the present invention; FIG. 9B is apictorial diagram illustrating one embodiment of the brand strategistdashboard in accordance with the present invention; and FIGS. 9C-9I areexemplary graphs that may be displayed on the brand strategist dashboardin accordance with the present invention.

FIG. 10 is a block diagram illustrating an example of a computer deviceon which computer-executable instructions to perform the methodologiesdiscussed herein may be installed and run.

DETAILED DESCRIPTION

A description of structural embodiments and methods of the presentinvention is provided with reference to FIGS. 1-10. It is to beunderstood that there is no intention to limit the invention to thespecifically disclosed embodiments but that the invention may bepracticed using other features, elements, methods, and embodiments. Likeelements in various embodiments are commonly referred to with likereference numerals.

The following definitions may apply to some of the elements describedwith regard to some embodiments of the invention. These terms maylikewise be expanded upon herein.

Brand Drug—refers to a medication, including prescription drugs, overthe counter (OTC) drugs, and supplements that are associated with aproprietary trade name, often have a trade mark, and that in many caseshave patents that provide monopolistic market protections for a finiteamount of time and/or intellectual property protections.

Brand Drug Advertisers—refers to manufacturers and retailers.

Brand Samples—refers to small quantities of free brand drugs provided bya brand drug advertiser for distribution by healthcare providers toconsumers or directly to consumers.

Brand Strategist—refers to a person who monitors or directs themonitoring of market and brand drug trends and oversees the planning,management and execution of brand drug analyses, planning, pricing,contracting, advertising and promotional campaigns and insures thattactics are being delivered to consumers, healthcare providers andpayors to achieve desired outcomes. The brand strategists may or may notbe an employee of the manufacturer.

Computational Model (also referred to as “computational modeling,”“computer model,” or “computer modeling”—refers to any software thatmodels an external process (such as a promotional campaign).

Consumer Segmentation—refers to the process of defining and subdividinga large homogenous group of consumers who are currently using or who arecandidates for using brand drugs into clearly identifiable groups havingsimilar demographics, needs, wants, demand characteristics or behaviorsfor the purpose of designing a segment promotional plan that matches theexpectations of consumers in the segments. In one embodiment, which isnot intended to limit the various constructions of a consumersegmentation, within this identified population the entire pool ofconsumers is subdivided based on sub-regions or segments that make upthe whole geography. For example, in California, a state may be dividedinto 25 segments, such as San Francisco, Oakland, San Diego, LosAngeles, Santa Barbara, etc.

Copay Card—refers a multiple use or single use tool through whichrebates and purchase discounts are offered to a consumer who uses, or isa candidate for, a particular brand drug. Copay cards come in many formsincluding plastic, paper or any electronic equivalent on a computer,smartphone, tablet or wearable device. Copay cards are most oftenfinanced by brand drug advertisers.

Coupon—refers to a voucher entitling the holder to rebates and purchase.Coupons come in many forms including plastic, paper or any electronicequivalent on a computer, smartphone, tablet or wearable device. Couponsare most often financed by brand drug advertisers

Direct to Consumer (DTC)—refers to a form of brand drug advertising thatis directed toward consumers, rather than healthcare professionals.

Distribution Channels—refers to networks of organizations, includingmanufacturers, brand drug advertisers, wholesalers, retailers andpharmacies supply brand drugs to consumers.

Elasticity Curve—refers to a measure used to show the responsiveness, orelasticity, of the ratio of the percentage change in at least onevariable to the percentage change in another variable.

Finite Post-LOE Phase—refers to a predetermined period of time as setforth by U.S. Patent Law that allows for certain brand drugs to exist onthe market with a restricted number of generic competitors.

Formulary—refers to a list or database of brand drugs. The main functionof a formulary is to specify the drugs that are approved to beprescribed by healthcare providers under a particular contract with apayor who provides a drug benefit plan to consumers. Consumers payvarying portions of the cost of the drug (known as a copay) forprescription drugs that are on formulary based on which drugs arepreferred by the payor. For drugs that are not on formulary, consumersmust pay a larger percentage of the cost of the drug, sometimes 100%.Formularies vary between drug plans and differ in the breadth of drugscovered and costs of copay and the drug insurance benefit premium. Mostformularies encourage generic substitution.

Healthcare Provider—refers to physicians, doctors, nurses, physicianassistants, dentists, optometrists, podiatrists, osteopaths, or anyindividual who has the state or federal government authority toprescribe drugs as well as those whose industry stature and influenceconstitute them as being brand drug thought leaders.

Healthcare Provider Segmentation—refers to the process of defining andsubdividing a large homogeneous group of healthcare providers. Oneembodiment of the physician segmentation is to divide the entirepopulation of physicians into clearly identifiable groups having similardemographics, needs, wants, demand characteristics or behaviors for thepurpose of designing a segment promotional plan that matches theexpectations of physicians in the segments. For example, within a givenpopulation of physicians, physicians who are surgeons may be segmentedas one group, and family doctors may be part of a different group orsegmentation.

Key parameter—refers to a parameter, typically numerically-valued in apredictive element or model or learning machine whose value affects thequality of the prediction. For instance, a key parameter can beassociated with each factor of a drug—cost, availability, genericcompetition (if any), and side effects—that a predictive model would usein order to generate a promotional tactic or segment promotional plan orpromotional campaign. Key parameters may be set manually from experienceor may be estimated by a learning machine.

Learning Machine—refers to a software system that creates a predictivemodel or more typically infers, refines and adapts the parameters of apredictive model based on past or current training data. Examples oflearning machines include decision trees, random forests, Bayesianclassifiers, neural networks, support vector machines, and logisticregression.

Loss of exclusivity (LOE)—refers to the expiration of patents granted bythe U.S. Patent and Trademark Office, which is calculated by standardduration of a patent plus any applicable Patent Term Adjustment (PTA).

Loyalty Cards (also known as Affinity Cards)—a plastic or paper card,visually similar to a credit card or debit card, or digital card thatidentifies the cardholder as a member in a loyalty or affinity program.By presenting the card, the purchaser is typically entitled to either adiscount on purchases, or points, credits, rewards, rebates or creditsthat can be used for current or future purchases or for merchandiserewards.

Manufacturers—refers to companies that research, develop, produce,and/or market drugs licensed for use as medications including but notlimited to pharmaceutical companies, biotech companies and consumerpackaged goods companies.

Optimal—inclusive of both the mathematical meaning of the best orhighest-valued outcome, and a looser general meaning of producing anoutcome better than others considered given a limited level ofcomputation or effort. It is often the case that optimality can beapproximated closer with increasing effort or computation (such as insubmodular functions,http://en.wikipedia.org/wiki/Submodular_set_function), but for practicalreasons, such as diminishing returns, the computation is halted with thebest results so far, and that result is labeled “optimal,” or “optimal”for the effort expended. This is also called “near optimal” or“approximately optimal” in the art.

Payor—refers to entities other than the consumer that finance orreimburse the cost of brand drugs. This term refers to PBMS, healthinsurance companies, other third-party payors or health plan sponsors(e.g. employers or unions).

Pharmacy Benefit Manager (PBM)—refers to third-party administrators ofprescription drug insurance benefit programs who are primarilyresponsible for processing and paying prescription drug insurance claimsand supplying prescription drugs via mail distribution channels toconsumers. PBMs also develop and maintain drug formularieshttp://en.wikipedia.org/wiki/Formulary, enter contract arrangements withpharmacies, and negotiate discounts and rebates with drug manufacturers.Currently a majority of Americans receive prescription drug benefitsadministered by PBMs.

Predictive—refers to generating an expectation of a future outcome basedon presently available information.

Predictive Element—refers to a computer model or program that, given aset of inputs, uses one or more methods internally to predict a tacticand/or outcome optionally with a weight or confidence score. Forinstance, given attributes of a certain consumer segment, such ascost-sensitivity, medial needs etc., a predictive element would generatea tactic (e.g. a way to reach best reach this targeted consumer segment)and optionally a measure of estimated effectiveness.

Predictive Model—refers to an automated or semi-automated process ofgenerating a prediction based on a model, typically combining softwareand data. The model may be programmed in software and its parameters(e.g. weights) modified or optimized by a learning machine or by adomain expert.

Promotional Campaign—refers to a combination of segment promotionalplans which consumer, healthcare provider, retailer, pharmacy or payorsegments are responsive to specific promotional tactic profiles deployedduring a brand drug product launch phase, growth phase, or around thebrand drug's loss of exclusivity phase.

Promotional Channels—refers to the physical distribution network orelectronic distribution networks (meaning computer communication mediaor handheld devices) through which brand drug promotional campaigns aredistributed to patients, consumers, healthcare providers and/or payors.

Promotional Mix—refers to specific combination of promotional methods dfor a brand drug. Elements of a promotion mix may include printadvertising, DTC advertising, digital advertising or other means ofadvertising.

Promotional Tactics—refers to a collection of tactics deployed to aparticular segment of consumers, healthcare providers or payorspertaining to a brand drug. Some popular promotional tactics include TVadvertisement, Internet advertisement, social networking advertisement,direct mail advertisement, presentations by sales representatives eitherby telephone, computer, mobile device or in person, and copay cards.

Retail (retail stores, retailer)—refers to pharmacies, supermarkets,grocery stores, big box stores and other retail outlets.

Sales Presentation—refers to detailed information about a product orproduct-line that is presented by a sales person or sales team face toface or electronically to a healthcare provider, PBM or payor for thepurpose of convincing the healthcare provider, PBM or payor to use orallow the use of a brand drug or set of brand drugs.

Segment Promotional Plans—refers to each segment promotional plancomprised of a collection of tactic profiles that have been determinedto be effective and responsive with a particular consumer segment,healthcare provider segment, retailer or payor segment.

Switch Data—refers to consumer and payor prescription transaction datacreated by certain systems technology companies for the purpose ofmanaging and monitoring the processing of prescription drug claims andclaims payment cycles. These data are often provided to or sold tohealthcare providers, pharmacies, wholesalers, retailers, PBMs, payersor others.

Tactic Profile refers to a promotional tactic to which a particularconsumer segment, healthcare provider segment, retailer segment or payorsegment responds, either positively, negatively or neutrally.

Quadripartite Model—refers to a computer-implemented combination of fourdifferent models or predictive elements that generates a joint orcombined prediction. For instance, a combination of a healthcareprovider element, a payor (e.g. insurance) element, and a consumer orconsumer group element.

System Architecture

FIG. 1 is a high-level block diagram illustrating one embodiment of abrand drug value growth and retention system 1 in a cloud computingenvironment 2 for conducting a predictive model for growing andretaining the value of brand drugs. The brand drug value growth andretention system 1 is coupled to a computer device 3 a of a brandstrategist 4 and through a network 5 to a computer device 3 of branddrug manufacturers 6, a computer device 3 of consumers 7, and otherintermediaries between the drug manufacturers 6 and the consumers 7.Each of the intermediaries, PBMs 8, pharmacies 9, retailers stores 10,doctors and hospitals 11, special pharmacies 12, wholesalers 13, anddirect mail prescription providers 14 has an associated respectivecomputer device 3 d, 3 e, 3 f, 3 g, 3 h, 3 i and 3 j. Collectively, thebrand drug manufacturers 6, the consumers 7, and the intermediariesoperate as cloud clients 15. The cloud clients 15 communicate with thebrand value growth and retention system 1 through the network 5, eitherwirelessly or via a wired connection. The retail stores 10 includesupermarkets, groceries, pharmacies and other retail segments.

The brand value growth and retention system 1 includes a computerprocessor 16 for executing a cloud operating system 17 and a brand drugvalue growth and retention engine 18, which are configured on a randomaccess memory (RAM) 19. An authentication module 20 is also part of thebrand drug value growth and retention system 1 for authenticating acloud client. The brand drug value growth and retention system 1 alsoincludes a virtual storage 21, which includes a virtual consumerdatabase 22 for storing consumer data, a virtual healthcare provider(HCP) database 23 for storing healthcare provider data, a virtual payordatabase 24 for storing PBM/payor segment data, and a virtual retaildatabase 24 b for storing retail data. The computer device 3 a has amaster dashboard 25, which displays data for viewing and assessing bythe brand strategist 4. The brand drug manufacturers 6, the consumer 7,and the intermediaries also have a dashboard 25 at their disposallocated in their respective computer devices.

The cloud system 2 is also referred to as web/Hypertext TransferProtocol (HTTP) server. Alternatively, the authentication module 20 canbe a separate server, which may employ a variety of authenticationprotocols to authenticate the user, such as a Transport Layer Security(TLS) or Secure Socket Layer (SSL), which are cryptographic protocolsthat provide security for communications over networks like theInternet. The protocols described herein are merely exemplary, andembodiments of the present invention include other emerging and newprotocols.

In one embodiment, the cloud computer system 2 is a browser-basedoperating system communicating through an Internet-based computingnetwork that involves the provision of dynamically scalable, and oftenvirtualized, resources as a service over the Internet, such as iCloud®available from Apple Inc. of Cupertino, Calif., Amazon Web Services(IaaS) and Elastic Compute Cloud (EC2) available from Amazon.com, Inc.of Seattle, Wash., SaaS and PaaS available from Google Inc. of MountainView, Calif., Microsoft Azure Service Platform (Paas) available fromMicrosoft Corporation of Redmond, Wash., Sun Open Cloud Platformavailable from Oracle Corporation of Redwood City, Calif., and othercloud computing service providers.

The web browser is a software application for retrieving, presenting andtraversing a Uniform Resource Identifier (URI) on the World Wide Webprovided by the cloud computer 2 or web servers. One common type of URIbegins with HTTP and identifies a resource to be retrieved over theHTTP. A web browser may include, but is not limited to, browsers runningon personal computer operating systems and browsers running on mobilephone platforms. The first type of web browsers may include Microsoft'sInternet Explorer, Apple's Safari, Google's Chrome, and Mozilla'sFirefox. The second type of web browsers may include the iPhone OS,Google Android, Nokia S60 and Palm WebOS. Examples of a URI include aweb page, an image, a video, or other type of content.

The network 5 can be implemented as a wireless network, a wired networkprotocol or any suitable communication protocol, such as 3G(third-generation mobile telecommunications), 4G (fourth-generationcellular wireless standards), long-term evolution (LTE), 5G, a wide areanetwork (WAN), Wi-Fi™ like wireless local area network (WLAN) 802.11n,or a local area network (LAN) connection (internetwork-connected toeither WAN or LAN), Ethernet, Bluebooth™, high frequency systems (e.g.,900 MHz, 2.4 GHz and 5.6 GHz communication systems), infrared,transmission control protocol/internet protocol (TCP/IP) (e.g., any ofthe protocols used in each of the TCP/IP layers), hypertext transferprotocol (HTTP), BitTorrent™, file transfer protocol (FTP), real-timetransport protocol (RTP), real-time streaming protocol (RTSP), secureshell protocol (SSH), any other communications protocol and other typesof networks like a satellite, a cable network, or optical networkset-top boxes (STBs).

The brand drug manufacturers 6 have various distribution channels todistribute brand drugs to the consumers 7. FIG. 1 shows one embodimentof such distribution channels, but the present invention is not limitedto this embodiment. Various distribution channels are also applicable.In one embodiment, after the pharmacy benefit manager (PBM) 8 receivesbrand drugs from manufacturers 6, the PBM 8 distributes brand drugs tothe pharmacy 9, which then sells the drugs to the consumers 7. The PBM 8may also deliver the drugs via the mail to consumers on behalf of healthplan providers.

A consumer obtains their brand drugs from a variety of sources, of whichsix exemplary sources are provided herein. The first source from whichconsumers can get their brand drugs is from a hospital 11 or prescribedby a doctor directly because the doctor office sometimes operates as apoint of sale location. The second source of distribution channel is apharmacy 9, such as CVS, Walgreens, independent pharmacies etc., wherethe consumer can purchase brand drugs. A third source from which theconsumer may obtain brand drugs is from a PBM 8. A fourth channel ofdistribution is a wholesaler 13, which buys large quantities of branddrugs to resell to retail stores, PBMs, physician offices, hospitals,consumers and others. A special pharmacy 12 provides a fifth source fordrug distribution. A sixth source of is retail stores 10 such as grocerystores, big box stores etc. Overall, manufacturers 6 have numerouschannels to distribute brand drugs to consumers and to ensure thatconsumers use the drugs as prescribed.

The distribution channels of drugs are becoming more integrated,offering brand drug advertisers, manufacturers 6 and consumers 7 moreactive and direct interactions. The majority of the consumers who havean insurance drug benefit get their drugs through a PBM. The PBM 8operates on behalf of payors and distributes the prescription drugs topharmacies 9, retail stores 10, hospitals 11 or directly to consumers 7.As a distribution channel, the PBMs 8 are an integrated delivery networkin which synchronized consumer information flows across differententities enabling a more direct communication between manufacturers 6and consumers 7. Additionally, PBM companies have the capability todistribute prescription infusible drugs because PBM companies may alsohave special distribution channels, such as specialty pharmacies 12 thatcarry infusible drugs and other specialty pharmaceutical products forcertain patients such as those with cancer, hemophilia, cystic fibrosis,organ transplant, etc. The PBMs 8 are highly automated and are able tooffer efficient service to consumers, including mailing orderprescriptions. Specialty pharmacies 12 also exist as independententities distinct and separate from PBMs.

In addition to being key components in the distribution infrastructure,drug manufacturers 6 are often able to control the pricing of the branddrugs sold to consumers 7 during the exclusivity period. The brand valuegrowth and retention system is used as a software tool to maintain aprescription drug's pricing for the drug manufacturers 6 during theperiod preceding and after the loss of exclusivity date. The brand drugvalue growth and retention engine also produces a promotional campaignafter the exclusivity period ends to retain the value of a branded drugfor the brand drug advertisers and manufacturers 6. During thedistribution process from the manufacturers 6 to the consumers 7, abrand drug value growth and retention system can be put in place, whichincreases the likelihood that a consumer will stay with a specific branddrug for a period of time, even after the brand drug has lost itsexclusivity. Essentially, the brand drug value growth and retentionengine creates a new period of commercialization, and its softwarefacilitates the retention of brand value for brand drugs after theexclusivity period has ended. Additionally, the brand drug value growthand retention engine software allows the storage of a massive amount ofconsumer information in the virtual storage 21. The virtual consumerdatabase that can also be linked to healthcare provider, retailer andpayor virtual databases 22, 23, 24, 25. In some embodiments, a consumerwould make a choice to opt-in to the consumer virtual database. In turn,in one embodiment the consumer virtual database and the HCP virtualdatabase are systems used to create retrospective analysis and theconstruction of a predictive model. In the HCP database, the datagathered from the physicians, third-party audited data aggregators ofphysician prescribing like IMS, NPA and others and third-partyaggregators of audited data of brand drug advertiser promotion activityis used to assess the brand drug use by consumers and, in turn, affectpromotional tactic profiles. The data is evaluated by focusing on thedifferent kinds of promotional activities that physicians report ashaving been directed to them, which allows the brand drug value growthand retention engine software to build a predictive model around theinformation to assess future behavior of physicians, prescribing changesand prescribing patterns. In the consumer system, the consumerinformation can be drawn from a wide variety of sources including butnot limited to data inputs from consumer behavioral databases likeAcxiom, consumer medical record databases, consumer record databases ofretail stores, pharmacies, PBMs, wholesalers and switch data companies,among others, which permit the user to plot not only the behavior ofconsumers but also the optimization of a predictive consumer model,revealing how consumers use or are likely to use a particular branddrug.

One skilled in the art will recognize that the brand value growth andretention system 1 implemented in the cloud computing environment 2illustrated in FIG. 1 is merely exemplary, and that the embodimentsdescribed herein may be practiced and implemented using many otherarchitectures and environments, such as a client-server platform.

Optionally, network security can be added to the cloud system (branddrug value growth and retention system) 1 in the cloud computingenvironment 2 to make the cloud system 1 secure and compliant. Networksecurity can be enhanced placing a firewall system between theprocessor/server 16 and the cloud clients 15. Additional networksecurity can also be enhanced using a client-side firewall system on thecloud clients 15. Moreover, the cloud system 1 can employ a backupmethod in compliance with SAS 70 and HIPAA requirements.

Software Architecture

FIG. 2 is a block diagram illustrating one embodiment of a brand drugvalue growth and retention engine 18. The brand drug value growth andretention engine 18 is a comprehensive software tool for optimizing apromotional campaign for a particular brand drug by considering amultitude of data inputs, including consumers, healthcare providers,payors, at least one predictive tool, and a feedback mechanism through alearning machine. The brand drug value growth and retention engine 18comprises several modules, including the financial model simulatormodule (and optional dashboard) 26, a consumer segments module 28 a, ahealthcare segments module 28 b, a retailer segments module 28 c, apayor segments module 28 d, the manufacturer copay card pricing module(optional report and optional dashboard) 29, the manufacturer brandexecution module 30, the manufacturer PBM/payor strategy module 31, themanufacturer PBM/payors execution module 32, the promotional campaignmodule 33, the sales presentations brand samples module 34, the mediamedical meetings module 35, the dashboard interface module 36, and a bus41. The payor segments module 28 d is configured to provide a predictivemodule that is used to predict the behavior of consumer and, especially,the predictive behavior around the propensity or likelihood thatconsumers use a copay card as a secondary source for paying for theirmedication. The predictive model in the consumer segments module 28 aoperates to predict when patients desire to use their drugs. Forpatients who are using the brand drug, the predictive model computes thepromotional spending mix in the actual dollar amount of what patientsare spending and how patients most effectively spend money around acopay card and other related factors. The healthcare provider segmentsmodule 28 b is configured to provide a predictive model of promotionalactivities directed to healthcare providers, which include physicians,nurse practitioners and physician assistants. The manufacturer PBM/payorstrategy module 31 is configured to provide a predictive model ofbehaviors of PBMs, insurance companies, and other payors includinggovernment payors. The term “copay card” refers generally and broadly toa consumer loyalty card, which can come in various forms, includingdigital coupons via a smartphone or a computer, a digital copay card, atraditional copay card, etc.

The financial model simulator module 26 is configured to receive one ormore predictive measures from the consumer segments module 28 a,healthcare provider segments module 28 b, and manufacturer PBM/payorstrategy module 31, as well as knowledge from investment decisions thatare made by experts to predict brand profitability and cash flows. Theoutput from the tool expresses the incremental brand drug unitsgenerated, increment brand drug revenue generated and the incrementalprofit generated. The financial model simulator module 26 continuouslyand interactively observes the incoming data associated with aparticular copay card and matched control groups of another company'scopay card to reveal the promotional output data. The output report willshow the profile of a consumer who is most likely to use a copay card.The output report also shows the profile of healthcare providers,specifically those which healthcare providers who use copay cards. Theoutput report will also show which consumers are already on a brand drugor which types of consumers are already on that company's drug, so thata drug manufacturer 6 will not spend money to formulate an advertisementcampaign to get these consumers on a copay card because these consumersare already using an identified prescription drug.

Process Flow

FIG. 3 is a pictorial representation of a brand drug lifecycle withseven key strategies. In one embodiment, a successful pharmaceuticalcompany advertiser typically attempts to grow a brand's revenue andprofit by applying seven strategies during its lifecycle, witheffectiveness measured by revenue and profitability along timedimensions. Initially, a prescription drug manufacturer creates a targetproduct profile at time t₁. Once a target product profile has beenestablished, clinical development and market development activitiesensue at time t₂. Subsequently, at time t₃, launch activities arestarted, which allows the segmentation of potential target consumers.The regional launch of a brand drug positions the brand drug in thelaunch market based on a selected promotional strategy and executiontactics. Launch of the drug in other regions around the globe generallyoccurs at time t₄ once the clinical development and market developmentplans for those regions have been completed and the necessary regulatoryapprovals have been garnered. At time t₅, the duration during the launchand growth phases of a brand drug, that consumer experience andengagement with the drug provides valuable data to create more consumerstrategies for growing the product. Finally, to grow a brand drug evenfurther, a pharmaceutical company often seeks new claims, indications,formulations and uses through additional clinical trials and newregional regulatory filings at time t₆. From target product profilethrough market development, launch and growth, a brand drug's lifecyclegenerally reaches its peak sales and later declines during a period inwhich either a competing drug entry takes market share from theestablished brand or the patent protection of a brand drug expiresleading to its Drug Industry Patent Cliff and its eventual loss ofexclusivity in the market at time t₇.

FIG. 4A is a flow diagram illustrating the effect of a predictive modelof the brand drug growth, value, a retention engine in pre-LOE period 37and a post-LOE period 38. The brand strategist 4 formulates a pre-LOEpromotional campaign(s) and a post-LOE promotional campaign(s) relativeto the patent expiration of a brand drug to generate and retain sales ofthe brand drug used by consumers prior to and leading up to the periodaround patent expiration. The application of the brand value growth andretention engine 18 produces the greatest amount of drug brand volumefor the least number of promotional campaign dollars and takes intoaccount the following promotional tactics: sales electronicpresentations, sales face-to-face presentations, formulary positions,DTC advertising, print advertising, mobile advertising (includingsmartphones, tablets, and wearable sensors), social network advertising,medical meetings for healthcare professionals, celebrity blogging,samples, reimbursement rates, rebates, discounts, secondary insurance inthe form of copay cards, loyalty cards, coupons, vouchers to name just afew.

One embodiment of the overall brand value growth and retention engine 18comprises a number of key steps as illustrated in FIG. 4 of the presentinvention. At time t₁, which in one example is about 18 to 24 monthsprior to the brand drug loss of exclusivity, the brand drug value growthand retention engine 18 is configured to generate a predictive modelthat produces a promotional campaign(s) prior to loss of exclusivity.Time t₁ is set by the brand strategist 4.

The predictive model processes a combination of the following data: thecomputational model of consumer segment data, the computational model ofhealthcare provider segment data and the computational model of payorsegment data. The predictive model identifies correlations that indicatethat certain combinations of consumer, healthcare and payor data arepredictive. Simultaneously, the brand strategist 4 begins promotionalplanning at t₂ to assure that resources, including dollars, people andprocesses, are secured to support the implementation of a promotionalcampaign designed from the output of the predictive model.

If the predictive model indicates the combined data are predictive, thebrand strategist 4 then deploys a promotional campaign at time t₃ thatis comprised of consumer segment promotional plans, healthcare providersegment promotional plans and payor segment promotional plans that arethe product of the computational models for consumers, healthcareproviders and payors.

In one embodiment, if the predictive model indicates the combined dataare not predictive, the output of the predictive model is used to selecta different segment promotional plan modified for a specified segmentwithin a target consumer group for feeding back iteratively to thecomputational model of consumer segment data, the computational model ofhealthcare provider segment data and the computational model of payorsegment data for further optimization until a combination of data isdeemed by the predictive model to be predictive.

Given that consumers, healthcare professionals and payors have changingneeds, wants, demands and behaviors, in the embodiment in FIG. 4 theapplication of the brand value growth and retention engine 18continuously operates and optimizes promotional campaigns until thebrand strategist 4 determines that he or she no longer desires topromote the brand drug in the market. In the embodiment as shown in FIG.4, the application of the brand value growth and retention engine 18continues for the finite post-LOE phase 38 of the brand drug. In thisembodiment the predictive model produces various promotional campaignsthat are generated based on segment promotional plans that are optimizedfrom t₁ through the period when multiple generics enter the marketduring the finite post-LOE phase of the brand drug in the market.

The predictive model determines segment promotional plans and whichpromotional tactic profiles require adjustment to yield a higherresponse rate at a certain investment level over time and therefore apromotional campaign relies on the learning machine in the predictivemodel to reveal which segment promotional plans are optimized or notoptimized.

The brand drug value growth and retention engine 18 is configured togenerate an optimal segment promotional plan(s) from the computationmodel segment data that are combined and processed through thepredictive model to generate a promotional campaign at time t₃ prior tothe loss of exclusivity based on the predictive model produced at timet₁.

One objective is to find correlations between a promotional tacticprofile and prescribing levels of a brand drug by physician segments.Promotional tactics can include the number of brand sales presentationsmade to a doctor, the number of medical meetings that the physicianattended, the number of brand samples that were provided to a doctor,and the number of copay cards provided to a doctor, among others. Theoptimal segment promotional plan(s) in a promotional campaign havetactic profiles that are directed to consumers, healthcare providers,and payors. In some instances, the computational models are runiteratively until there is sufficient data, and the predictive model issufficiently developed to deem the output predictive. After thepredictive model is deemed predictive, the overall promotional campaignwill most likely have the highest impact, which provides the highestbrand drug volume for the least amount of promotional dollars.

Even after the promotional campaign has been launched, the predictivemodel continues to operate, continues to receive new data, and continuesto refine and modify the parameters of the predictive models. A curve 39represents the iterative and continuous running of predictive models torefine, modify, transform and improve an optimal promotional campaign,which over time is intended to increase the sales of brand drugs used bythe consumers, as shown in a first population of consumers using branddrug 40 a, a second population of consumers using brand drug 40 b thatis larger than the first population size, and a third population ofconsumers using brand drug 40 c that is larger than the secondpopulation size.

To better select a tactic profile, to which a consumer 7 may be moreresponsive, the brand strategist 4 attempts to understand the segmentsof consumers including their needs, wants, demands and behaviors.Depending on what a particular segment of consumers will respond to, thebrand strategist 4 selects effective consumer segment promotional plans,which, combined with healthcare provider segment promotional plans andpayor segment promotional plans, constitute a brand drug promotionalcampaign by operating through a predictive model at 42. Similarly, thebrand drug value growth and retention engine 18 is configured to collectcomputational model data from physician segments, analyzing physicianprescribing behavior, and analyzing the data relative to the promotionaltactics a drug manufacturer 6 has deployed against a particularphysician. Additional considerations can include sales calls from salesrepresentatives and the number of educational programs that physiciansattend. Similarly, the brand drug value growth and retention engine 18is configured to collect computational model data from payor segments,analyzing payor behavior, and analyzing the data relative to thepromotional tactics (including pricing and discounting) a drugmanufacturer 6 has deployed against a particular payor.

Modified promotional tactics producing different segment promotion plansbegin at time t₃, which in one embodiment of the present invention is atime duration closer to the loss of exclusivity relative to t₁ and t₂. Apromotional campaign comprises a plurality of segment promotional plans.Each segment promotional plan is directed to a particular segment ofconsumers, a particular segment of healthcare providers and/or aparticular segment of payors with specific tactics to which a respectivesegment responds. Different segments of consumers, segments ofhealthcare providers and segments of payors may have the same ordifferent sets of promotional tactics that are applied in order to havethe predictive model produce an effective promotional campaign.

In one embodiment, the brand value and retention engine 18 is configuredto optimize promotion at the time of loss of exclusivity. A brand drugcompany may significantly increase its brand drug direct to consumeradvertising including but not limited to TV advertising, printadvertising, copay cards, loyalty cards, coupons etc. This is donethrough several different promotional channels, including, but notlimited to, electronic mail, physical mail, video push, mobile deviceadvertising and the use of copay cards. The aim in this embodiment is toencourage consumers to speak to their doctors about starting therapy ona brand drug or to remain loyal to brand drugs that they have beenusing. The increase in direct to consumer advertising is intended toretain more consumers on the brand drug and thus increase and retainbrand drug volume before the loss of exclusivity, after the loss ofexclusivity even months after the loss of exclusivity.

While not available to all drugs, sometimes a brand drug will bedesignated as one that is granted the legal rights to have only onecompeting single source generic drug at the time of loss of exclusivityof the brand. This designation would have occurred early in the brand'slife.

A brand drug is typically price discounted immediately after the drug'sloss of exclusivity 43 so the brand drug can remain price competitivewith the single or other available generic drugs. Therefore, if aconsumer 7 views the brand drug and the generic drug to be comparable inprice and effectiveness and safety, they are more likely to be open toremaining on the branded drug after loss of exclusivity.

The negotiation for brand formulary position and pricing 44 with PBMs 8is another key factor to ensure patients can get access to the brandeddrug before and after the loss of exclusivity. To ensure the PBM 8patients have access to the brand, manufacturers negotiate arrangementsthat provide brand drugs at discounted prices to remain pricecompetitive with generic drugs. Without such negotiated arrangements, aPBM may automatically switch patients from the brand drug to a genericdrug upon the expiration of the exclusivity period. For example, theprice of a brand drug could decrease by 20% to 30% relative to the pricebefore the loss of exclusivity period in order to remain pricecompetitive with a generic drug at 45, which the population size of theconsumers is reduced relative to the third population size of consumersusing the brand drug.

Often market share of the brand drug will fall after the loss ofexclusivity. A drug manufacturer aims to retain significant market shareup to and post loss of exclusivity. In time, multiple sources of genericdrugs will often come to one market, which will place growing pricepressure on expired brands s and can cause the manufacturer to continueto reduce the price of the brand drug to remain competitive withgenerics.

FIG. 4B is a flow diagram illustrating the effect of a predictive modelof the brand drug growth, value, a retention engine in launch phase of abrand drug. The brand strategist 4 formulates a launch promotionalcampaign(s) to generate and retain brand drug use at launch and postlaunch and prior to a brand's growth phase. The application of the brandvalue growth and retention engine 18 produces the greatest amount ofbrand drug sales for the least amount of promotional campaign dollarsand takes into account the following promotional tactics: saleselectronic presentations, sales face-to-face presentations, formularypositions, DTC advertising, print advertising, mobile advertising(including smartphones, tablets, and wearable devices), social networkadvertising, medical meetings for healthcare professionals, celebrityblogging, samples, reimbursement rates, rebates, discounts, secondaryinsurance in the form of copay cards, loyalty cards, coupons to namejust a few.

One embodiment of the overall brand value growth and retention engine 18comprises a number of key steps as illustrated in FIG. 4B of the presentinvention. At time t₁, which in one example is about 3 to 9 months priorto the brand drug, the brand drug value growth, and retention engine 18is configured to generate a predictive model that produces a promotionalcampaign(s) for use at the time of brand launch. Time t₁ is set by thebrand strategist 4.

The predictive model processes a combination of the following data:computational model of consumer segment data, the computational model ofhealthcare provider segment data and the computational model of payorsegment data. The predictive model identifies correlations that indicatethat certain combinations of consumer, healthcare, and payor data arepredictive. Simultaneously, the brand strategist 4 has beginspromotional planning at t₂ to assure that resources, including dollars,people, and processes, are secured to support the implementation of apromotional campaign designed from the output of the predictive model.

If the predictive model indicates the combined data are predictive, thebrand strategist 4 then deploys a promotional campaign at time t₃ thatis comprised of consumer segment promotional plans, healthcare providersegment promotional plans and payor segment promotional plans that arethe product of the computational models for consumers, healthcareproviders and payors.

In one embodiment, if the predictive model indicates the combined dataare not predictive, the output of the predictive model is used to selecta different segment promotional plan modified for a specified segmentwithin a target consumer group for feeding back iteratively to thecomputational model of consumer segment data, the computational model ofhealthcare provider segment data and the computational model of payorsegment data for further optimization until a combination of data isdeemed by the predictive model to be predictive.

Given that consumers, healthcare professionals and payors have changingneeds, wants, demands and behaviors, in the embodiment in FIG. 4B theapplication of the brand value growth and retention engine 18continuously operates and optimizes promotional campaigns until thebrand strategist 4 determines that he or she no longer desires topromote the brand drug in the market. In the embodiment as shown in FIG.4B, the application of the brand value growth and retention engine 18for the brand drug launch phase continues for the launch phase 37 b ofthe brand drug. The launch phase is defined by the brand strategist. Inthis embodiment the predictive model produces various promotionalcampaigns that are generated based on segment promotional plans that areoptimized from t₁ through the period when the brand strategistdetermines that the launch phase of the brand drug has concluded.

The predictive model determines segment promotional plans and whichpromotional tactic profiles require adjustment to yield a higherresponse rate at a certain investment level over time and therefore apromotional campaign relies on the learning machine in the predictivemodel to reveal which segment promotional plans are optimized or notoptimized.

The brand drug value growth and retention engine 18 is configured togenerate an optimal segment promotional plan(s) from the computationmodel segment data that are combined and processed through thepredictive model to generate a promotional campaign at time t₃ launchphase based on the predictive model produced at time t₁.

One objective is to find correlations between a promotional tacticprofile and prescribing levels of a brand drug by physician segments.Promotional tactics can include the number of brand sales presentationsmade to a doctor, the number of medical meetings that the physicianattended, the number of brand samples that were provided to a doctor,and the number of copay cards provided to a doctor, and among others.The optimal segment promotional plan(s) in a promotional campaign havetactic profiles that are directed to consumers, healthcare providers,and payors. In some instances, the computational models are runiteratively until there is sufficient data, and the predictive model issufficiently developed to deem the output predictive. After thepredictive model is deemed predictive, the overall promotional campaignwill most likely have the highest impact, which provides the highestbrand drug sales for the least amount of promotional dollars. Even afterthe promotional campaign has been launched, the predictive modelcontinues to operate, continues to receive new data, and continues torefine and modify the parameters of the predictive models. A curve 39represents the iterative and continuous running of predictive models torefine, modify, transform and improve an optimal promotional campaign,which over time is intended to increase the volume brand drugs used bythe consumers, as shown in consumers in first population size usingbrand drug 40 a, consumers in a second populations size using the branddrug 40 b with a second population size that is larger than the firstpopulation size, and consumers in a third population size using branddrug 40 c with a third population size that is larger than the secondpopulation size.

To better select a tactic profile, which a consumer 7 may be moreresponsive, the brand strategist 4 attempts to understand the segmentsof consumers including their needs, wants, demands and behaviors.Depending on what a particular segment of consumers will respond to, thebrand strategist 4 selects effective consumer segment promotional plans,which combined with healthcare provider segment promotional plans andpayor segment promotional plans, constitute a brand drug promotionalcampaign by operating through a predictive model at 42. Similarly, thebrand drug value growth and retention engine 18 is configured to collectcomputational model data from HCP segments, analyzing HCP prescribingbehavior, and analyzing the data relative to the promotional tactics, adrug manufacturer 6 has deployed against a particular physician.Additional considerations can include sales calls from salesrepresentatives and the number of educational programs that physiciansattend among other promotional tactics. Similarly, the brand drug valuegrowth and retention engine 18 is configured to collect computationalmodel data from payor segments, analyzing payor behavior, and analyzingthe data relative to the promotional tactics (including pricing anddiscounting) a drug manufacturer 6 has deployed against a particularpayor.

Modified promotional tactics producing different segment promotionplans. A promotional campaign comprises a plurality of segmentpromotional plans. Each segment promotional plan is directed to aparticular segment of consumers, a particular segment of healthcareproviders, and/or a particular segment of payors with specific tacticsto which a respective segment responds. Different segments of consumers,segments of healthcare providers, and segments of payors may have thesame or different sets of promotional tactics that are applied in orderto have the predictive model produce an effective promotional campaign.In one embodiment, the brand value and retention engine 18 is configuredto optimize promotion at the time launch. A brand drug company maylaunch a brand drug with very high spending aimed at direct to consumeradvertising including but not limited to TV advertising, printadvertising, copay cards, loyalty cards, coupons etc. This is donethrough several different promotional channels, including but notlimited to, electronic mail, physical mail, video push, mobile deviceadvertising and the use of copay cards. The aim in this embodiment is toencourage consumers to speak to their doctors about starting therapy ona brand drug or switching from another brand drug that they have beenusing. The increase in direct to consumer advertising is intended tocapture more consumers on the brand drug and thus increase brand drugvolume during the launch phase.

The negotiation for brand formulary position and pricing 44 with PBMs 8is another key factor to ensure patients can get access to the brandeddrug at launch. To assure the PBM 8 patients have access to the brand,manufacturers negotiate arrangements that provide branded drug atdiscounted prices to remain price competitive with other branded drugs.Often without such negotiated arrangements, a PBM may automatically denyits patients access to the brand drug at launch, instead requiringpatients to use cheaper brand drugs or generics.

FIG. 5 is a diagram illustrating one embodiment of a predictive modelmethod executed by the brand drug value growth and retention engine 18.At step 46, the brand drug value growth and retention engine 18 isconfigured to determine a predefined or computed threshold for aprospective brand. The threshold for a prospective brand engagement, inone embodiment, can be dictated by time to LOE, a competitive market,brand revenue, brand marketing spending, and brand profit. If theprospective brand does not meet the threshold, the process remains atstep 46. Upon meeting the threshold for a brand engagement, the branddrug value growth and retention engine 18 proceeds to execute acomputational model on consumer segments in step 47, execute acomputational model on healthcare provider segments in step 48, andexecute a computational model on payor segments in step 50. Steps 47,48, 49, 50 can occur in parallel or in some combination in thisembodiment of the present invention. At step 47, the consumer segmentsmodule 28 a is configured to run a computational model on consumersegments to determine an optimal promotional plan for consumers who arecandidates for a particular brand drug. The computational models onconsumer segments are built based on various attributes of individualconsumers or subgroups including but not limited to the consumer's useof copay cards, their medical conditions, prior prescription brand drugpurchases, prior OTC brand drug purchases, insurance carriers, preferredretail stores, their buying patterns, shopping patterns, income levels,gender, race, educational levels, among others. The number and types ofattributes in a computational model of consumer segments are dependentand can be adjusted based on several factors including but not limitedto a set of predetermined attributes at the outset of a promotionalcampaign, modifying the attributes from a source during a promotionalcampaign, the result of a predictive model, or the output from thelearning machine.

At step 48, the healthcare provider segments module 28 b is configuredto run a computational model on healthcare providers who treat consumersare users or who are candidates using the particular brand drug. In thisembodiment, the term healthcare provider segments refers to optimizingindividual healthcare providers or subgroups within segments as asegment, rather than by geographical segmentation. At step 49, thehealthcare provider segments module 28 b is configured to run acomputational model on individual or subgroups of retail segments todetermine an optimal promotional mix for retailers that provide thebrand drug (or drug brand X) to consumers.

At step 50, the manufacturer PBM/payor strategy module 31 andmanufacturer PBM/payors execution module 32 are configured to run acomputational model on individual or subgroups of payor segments, suchas PBMs and insurers, to determine optimal contracting strategies forthe particular brand drug. Data intelligence for selecting a particularpromotion campaign can be sourced from the computational model onconsumer segments at step 47, the computational model on healthcareprovider segments at step 48, and the computational model of payorsegments at step 50. The promotional campaign is not only based on thecosts of advertising, but it also considers formulary position and therelated pricing associated with a brand's formulary position, pricingfor a brand manufacturer and the optimal pricing for patients byconsidering the different promotional mixes for consumers and individualhealthcare providers and by matching that to an optimizing rebating anddiscounting strategy.

In one embodiment, pertaining to formulary positions, a PBM may makeavailable a number of prescription drugs intended to treat a specificdisease or disorder. The PBM may allow one of the drugs to be widelyavailable to their patients because of the negotiated rebates anddiscounts extended by the manufacturer and the broad benefits determinedby the PBM medical authorities. In this embodiment, the PBM may requireno insurance copay for this drug as a way to encourage physicians andpatients to use this preferred drug. As an alternative to the preferreddrug, alternative drugs may be made available with different rebates anddiscounts. For these drugs, patients may be charged an insurance copayor may have to pay for the full cost of the drug with no contributionfrom their insurance company. The different levels of rebates,discounts, and copay pricing provide different variables to produce anoptimal brand drug formulary position.

At step 51, the financial model simulator module 26 in the brand valueand retention engine 18 is configured to compute and generate acombination predictive model in this embodiment. The financial modelsimulator module 26 receives the computational model on consumersegments from step 47, the computational model on healthcare providerssegments from step 48, the computational model on the retail storesegments from step 49, and the computational model on payor segments instep 50 to run a predictive model of these input data to generate anoptimal promotional campaign for the specified brand drugs. Datareceived by the financial model simulator module 26 should besufficiently large to make the predictive model meaningful. Continuousstreams of consumer segment data, healthcare provider data and payorsegment data are fed into the predictive model. In some embodiments, thepredictive model is computed based on receiving two or morecomputational models from among the four possible computational models,i.e., the consumer segments model in step 47, the healthcare providersegments computational model in step 48, the retail store segmentscomputational model in step 49, and the payor segments computationalmodel in step 50. In other embodiments, the predictive model is computedbased on receiving one or more computational models from among the fourpossible computational models in steps 47, 48, 49, 50.

In some embodiments, the predictive model combines various computationalmodels are executed in a way that is complaint with present governmentregulations, like HIPPA, or future government amendments orlegislations.

At step 52, the brand value growth and retention 18 stores, accumulatesand retrieves prior promotional campaigns and their results, for bothpromotions of different earlier products and any prior campaigns andtheir results for the current product.

At step 53, the brand drug value growth and retention engine 18 isconfigured to invoke machine learning methods in real time or recentdata to estimate and attempt to optimize the parameters for predictionof one or a plurality of computational models such as those in steps 47,48, 49, 50. The specifics of the implementation of machine learningmethods employed are known in the literature. For additional informationon the machine learning methods, see Michalski, R., J. Carbonell, and T.Mitchell (1986), Machine Learning: An Artificial Intelligence Approach,Volume II, Morgan Kaufman Publishers: Los Angeles; Bishop, C. M. (2006),Pattern Recognition and Machine Learning, Springer; Singh, Y., P. K.Bhatia, O. Sangwan (2007), A Review of Studies of Machine LearningTechniques, International Journal of Computer Science and Security,Volume (1): Issue (1), 70-84, which are incorporated by reference as iffully set forth herein. Machine learning methods may include, forexample, logistic regression, support vector machines, decision trees,random forests, max-entropy classifiers, re-enforcement learning,genetic algorithms, neural networks or other known or new methods. Mostof these methods are based on Bayesian statistics and use prior data(e.g. prior campaigns) and prior results (e.g. failure, success, degreeof partial success) of said prior data (e.g. campaigns or individualtactics within the campaigns) to improve the weights and otherparameters in the predictive models. For additional information onspecifics of Bayesian statistics, see Spiegelhalter D. and K. Rice(2009), Bayesian statistics, Scholarpedia; Bolstad, W. (2007),Introduction to Bayesian Statistics, 2nd Ed., John Wiley & Sons: NewJersey; Bishop, C. M. (2006), Pattern Recognition and Machine Learning,Springer, which are incorporated by reference as if fully set forthherein.

Step 53 may also include active or proactive learning where a new tacticor new campaign (e.g. advertising via social media such as Facebook, orvia mobile apps on smartphones) may be tried in order to jointlyoptimize both new knowledge gained about the effectiveness of the newcampaigns or tactic and the immediate impact of the selected campaignsand tactics. The former may be viewed as longer-term or amortizedbenefit, whereas the latter is the current benefit predicted by thecomputational model(s). Proactive learning takes into account the costand risk of experimental campaigns, and this would be a novelapplication area for such machine learning systems. For additionalinformation on specifics of active or proactive learning, see B. Settles(2012), Active Learning: Synthesis Lectures on Artificial Intelligenceand Machine Learning, Morgan & Claypool; Donmez, P., Carbonell, J.(2008), “Proactive Learning: Cost-Sensitive Active Learning withMultiple Imperfect Oracle,” in Proceedings of the 17th ACM Conference onInformation and Knowledge Management (CIKM '08), Napa Valley; Donmez, P.and Carbonell, J. (2008), “Optimizing Estimated Loss Reduction forActive Sampling in Rank Learning,” in Proceedings of the InternationalConference in Machine Learning, which are incorporated by reference asif fully set forth herein.

At step 54, the brand drug value growth and retention engine 18 isconfigured to allow for re-estimating one or more computational modelsif their results were not positive. For instance, if the machinelearning method suggested two potential but mutually exclusiveimprovements, one of which was attempted without positive results, thecomputational model may then be re-estimated with the second improvementand fed back into the overall system.

At step 55, the brand drug value growth and retention engine 18 isconfigured to determine the benefit (e.g. improvement in sales) of thepromotional campaign and feeds back to the learning machine (step 53)directly if positive to re-enforce, or indirectly via step 54(re-estimation), and re-runs the computational models to inform thelearning machine that certain predictions need revision.

As an example of a promotional campaign, FIG. 6A is a table illustratinga promotional campaign from a collection of segment promotional plans,represented in this embodiment as a table 59 stored in a database. Thetable 59 comprises vertical columns on different segments 60 ofconsumers (or segments of healthcare providers). The horizontal rowsdenote different promotional tactics 61 directed to the consumersegments 60. For the first consumer segment 62, among the differentpromotional tactics 61 from 1 to n as applied to the first consumersegment 62, the first consumer segment 62 responds well to the secondtactic profile 63 b, the third tactic profile 63 b, and the seventhtactic profile 63 c. The identified promotional tactics 63 a, 63 b, 63c, which have been determined to be effective promotional tacticsdirected to the first consumer segment 62, are collectively referred toas a first segment promotional plan SPP1 64. The brand drug value growthand retention engine 18 is configured to determine the number of tacticprofiles that are effective against a particular consumer segment,resulting in a series of segment promotional plans: SPP1 64 for thefirst consumer segment, SPP2 66 for the second consumer segment, SPP3 67for the third consumer segment, and SPPn 68 for the n consumer segment.A promotional campaign 65 comprises a plurality of segment promotionalplans, represented by the following equation where timing of segments isregulated by the function α(t₁), where in one embodiment the functioncan be a sequence, in another all in parallel, and in other embodimentsany partially or fully sequential or parallel segments:

${PC} = {\sum\limits_{{i = 1},N}\; {{\alpha \left( t_{i} \right)}{SPP}_{i}}}$

In one embodiment, the promotional campaign may include explicitinteraction terms among the actual or planned segment promotional plansas well as individual segment promotional plans (SPPs). The explicitinteraction terms can be binary, comprising any planned or actual SPPinteracting with any other planned or actual SPPs. The terminology“explicit interaction terms” may refer to a term that combines two ormore variables in a potentially non-linear way, e.g. G(X₁, X₂),especially if used inside another equation, such as an otherwise linearequation on X₁ and X₂ is an explicit interaction term between X₁ and X₂.The function G can be anything meaningful in the application area. Forinstance, G can be a product X₁*X₂ or a ratio X₁/X₂, or something suchas a transformed sum, e.g. LOG(X₁)+LOG(X₂). For example, the magnitudeof one campaign element may be three times larger than that of adifferent campaign element, e.g. G(C1, C2)=Cost(C1)/Cost(C2)=3, withouthaving to specify the actual value of either Cost(C1) of Cost(C2), justtheir relative magnitudes.

Returning to step 51 in FIG. 5, the financial model simulator module 26is configured to compute a segment promotional plan by consideringseveral key variables, including coefficients, promotional tactics,segments and frequency. In one formulation, each tactic profile in aparticular segment promotional plan is determined by the weightedfactors of the coefficients multiplied by the frequency in which thetactic is displayed for a particular segment as represented by thefollowing equation:

${SPP} = {\sum\limits_{{i = 1},m}\; {{\alpha \left( t_{i} \right)}\beta_{i}{T_{i}\left( {F_{i},S_{j}} \right)}}}$

where β₁*T₁(F₁, S_(j)) denotes the first tactic profile, β₂*T₂(F₂,S_(j)) denotes the second tactic profile, β₃*T₃(F₃, S_(j)) denotes thethird tactic profile, and so on up to the n^(th) tactic profile, where nis the total number of profiles. The coefficients β_(i) are weightedfactors applying respectively to the first tactic profile, the secondtactic profile and the third tactic profile. The first term, β_(i)*T₁(F₁S_(j)), represents the frequency F₁ in applying the first promotionaltactic T₁ to a first consumer segment S_(j). The second term, β₂*T₂(F₂,S_(j)), represents the frequency F₂ in applying the second promotionaltactic T₂ to first consumer segment S_(i) and so on up to the m^(th)tactic, where m is the total number of tactics. The α(t)'s again denoteany ordering or parallelizing temporal function. Although, forsimplicity, the equation is presented as a summation, which is only oneembodiment of the invention; the summation may be replaced by any otherconstructive combination function. Tactic profiles may indicate anymanner of promotion, including but not limited to traditional media,social media, consumer-direct and payor direct programs.

If the result of the predictive model is negative, the process returnsto steps 47, 48, and 50 for conducting real time or recent datapredictive analysis. If the financial model simulator module 26determines that the data is considered predictive, then at step 56 thebrand drug value growth and retention engine 18 is configured todetermine if a change in promotional campaign is desired at this time.The current promotional campaign continues if there are no changes inthe promotional campaign, as is shown in step 57. However, if a changein a promotional campaign is desired, the brand drug value growth andretention engine 18 is configured to select a different promotionalcampaign for a specified segment within a targeted consumer-based groupat step 58. At step 53, the brand drug value growth and retention engine18 continuously trains a learning machine as a feedback mechanism toimprove and optimize the predictive model of promotional campaigns.

In an alternative embodiment as shown in FIG. 7, the predictive modelmethod can be a cumulative predictive model at step 51 based on anindividual predictive model, which the financial model simulator module26 is configured to determine whether the consumer data is predictive atstep 101, whether the healthcare provider data is predictive at step102, and whether the payor segment data is predictive at step 103. Thisformulation can be represented mathematically by the following equation:

Cumulative Predictive model=F(P(M_(C)),P(M_(HCP)),P(M_(P)),P(M_(R)))

where P(M_(C)) denotes the predictive model consumer segment data instep 101, P(M_(HCP)) denotes the predictive model of healthcare providersegment data in step 102, P(M_(P)) denotes the predictive model of payorsegment data in step 103, and P(M_(R)) denotes the retail-sales-basedpredictive model in step YYY. For example, the predictive model wouldevaluate a run on 30 segments of patients, 30 segments of physicians,and 15 possible rebating structures. The function F is any combinationfunction and in its simplest embodiment would be additive, e.g. aweighted sum.

In one embodiment the predictive model uses data from retail sales ofpharmaceutical products gathered from consumers with affinity cards atpharmacies or other retail outlets. This data is typically normalized toaccount for the fraction of the population that uses affinity cards ineach major geographical region or with each large-scale retailer, andfor other factors (age differences, health plans, etc.). This predictivemodel based on actual sales is then used to estimate trends (e.g.increased sales of certain products or product categories, seasonalvariations, etc.), and these trends plus current values are used toestimate the expected sales. A more complex embodiment would include theeffects of marketing efforts combined with baseline trend prediction.The prediction in such an embodiment may be estimated by the followingfunction or by other methods of combining similar information:

${P\left( M_{R} \right)} = {\sum\limits_{X_{i} \in R}\; {{N\left( X_{i} \right)}{{Sales}\left( X_{i} \right)}\left( {1 + \frac{d\left( {{Sales}(X)} \right)}{dt}} \right)}}$

In the above function we have a set of retail products R={X_(i), X₂, . .. X_(n)}, measured sales volume of each X_(i), normalization N(X_(i)) toaccount for the expected fraction of sales actually recorded (N=theinverse of that fraction), and adjusting for increasing or decreasingtrends as measured over a time interval t.

In one embodiment, the predictive model combines information fromcomputational models of the consumer segment data, healthcare providersegment data, the retail segment data and the payor segment data in alinear manner or a substantially linear manner. The combined informationin the predictive model provides explicit weights to one or morecomponents in the combined information. The term “linear” is commonlyused in the art and may refer to the elements (variables, components,sub-components) that are combined via an additive process, possibly withweights. The term “substantially linear” refers to a linear equationthat could still have a small corrective factor that may be non-linear.For instance, a linear combination of variables X₁ X₂, X₃, and X₄ can berepresented mathematically by Y=A₁X₁+A₂X₂+A₃X₃+A₄X₄, where A₁ is acoefficient (a weight) assigned to X₁, A₂ is a coefficient for X₂, A₃ isa coefficient for X₃, and A₄ is a coefficient for X₄. Explicit weightsare the A₁, A₂, A₃, A₄ above, i.e. the coefficients.

In some embodiments, the predictive model in step 51 is computed basedon receiving two or more computational models from among the fourpossible computational models, i.e., the consumer segments model in step47, the healthcare provider segments computational model in step 48, theretail store segments computational model in step 49, and the payorsegments computational model in step 50. To phrase it in another way,the predictive model in step 51 is computed based on receiving two ormore predictive elements from among the four possible predictiveelements, i.e., the predictive element for the consumer segments modelin step 101, the predictive element for the healthcare provider segmentscomputational model in step 102, the predictive element for the retailstore segments computational model in step 145, and the predictiveelement for the payor segments computational model in step 103. In otherembodiments, the predictive model is computed based on receiving one ormore computational models from among the four possible computationalmodels in steps 47, 48, 49, 50, or receiving one or more predictiveelements among the four predictive elements in steps 101, 102, 145, 103.

Embodiments of the present invention include a promotion campaign thatis represented by the following augmented equation, where j=1, M rangesover all segment promotional plans, including those of other active orplanned promotion campaigns and the function g(SPP_(i),SPP_(j)) computesinteractions among the campaign plan portions if any:

${PC} = {\sum\limits_{{j = 1},M}\; {\sum\limits_{{i = 1},N}\; {{\alpha \left( t_{i} \right)}\left\lbrack {{SPP}_{i} + {g\left( {{SPP}_{i},{SPP}_{j}} \right)}} \right\rbrack}}}$

For example, two promotional campaigns for the same medication mayinteract. For instance, their message should normally be consistent,e.g. “more effective treatment” (vs “cheaper” vs “easier to take”).Alternately, two concurrent campaigns for different medications may beoptimized by combining promotion plan segments. For instance, the samemailing may contain two fliers for treating age-related ailments andstate that medications may be taken together—e.g. an anti-inflammatoryand a skin-rejuvenation ointment. Alternatively, two drugs for the sameailment may confuse the market, and therefore they should not bepromoted simultaneously. These examples are meant to be illustrative andnot limiting as to the range of possible positive and negativeinteractions among promotion campaigns (PCs) and their segmentpromotional plans (SPPs). The function g(SPPi,SPPj) computes theinteraction and can have a positive value (e.g. cost savings bycombining mailings) or a negative value (e.g. two statin drugs promotedin parallel campaigns targeted at the same consumers, confusing them).Thus, g modulates the campaigns in order to optimize their combination,not just each campaign independently. In some embodiments, thepromotional campaign PC is a weighted combination of the segmentpromotional plans (SPPs). The term “weighted combination” may refer to“linear” and implies that the weights (coefficients) for each variableare normally different from each other.

Optionally, the computational model on consumer segments, thecomputational model on healthcare provider segments, and thecomputational model on payor segments can also be modified based onother variables including to changes in reimbursement, changes indistribution, and consumer health dynamics. Embodiments of the presentinvention also include an online promotional campaign over a web browseror a mobile device that is personalized and directed to an individualconsumer, rather than a consumer segment. Furthermore, embodiments ofthe present invention also include mechanisms for consumers to providefeedback on the brand drug using online and mobile tools and deviceslinked to social platforms like YouTube, Facebook, Twitter andPinterest.

One embodiment of the overall brand value growth and retention engine 18comprises a number of key steps as illustrated in FIG. 7 of the presentinvention. In this embodiment, a grocery store chain desires to launch apromotional campaign intended to increase the sales of their store chainbrand of baby aspirin.

The overall brand value growth and retention engine 18 processes acombination of the following data: the computational model of consumersegment data at step 47 to identify individual segments or sub-segmentsof consumers who have been diagnosed or are at risk of developing heartattacks and strokes; the computation model of payor segment data at step50 to identify individual segments or sub-segments of consumer babyaspirin users or candidates for baby aspirin by analyzing medical claimsdata diagnosis codes for diseases typically found in consumers who havehad a prior heart attack or stroke or who are at risk of having a heartattack or stroke, in addition to other data sources in a HIPPA compliantfashion; the computation model of healthcare segment data at step 48 toidentify individual segments or sub-segments HCPs who treat patientswith or at risk of developing heart attacks or strokes by analyzing dataon the prescribing history of individual segments or sub segments ofhealthcare providers using multiple sources including, but not limitedto switch data, prescriber audit data and other data sources; thecomputational model of retail segment data at step 49 aiming to identifyindividual segments or sub-segments consumers who have made priorpurchases of the store chain brand baby aspirin and/or other brands ofbaby aspirin.

The brand drug value growth and retention engine is configured toanalyze customer segments and sub-segments data to identify correlationsthat indicate that certain combinations of customer segment data arepredictive. The brand drug value growth and retention engine 18 isconfigure to separately analyze healthcare provider segments andsub-segments data to identify correlations that indicate that certaincombinations of healthcare provider segment data are predictive. Thebrand drug value growth and retention engine separately analyzes retailstore segments and sub-segments data to identify correlations thatindicate that certain combinations of retail segment data arepredictive. The brand drug value growth and retention engine 18 isconfigured to separately analyze payor segments and sub-segments data toidentify correlations that indicate that certain combinations of payorsegment data are predictive.

The brand drug value growth and retention engine 18 then is configuredto combine the predictive outputs 101, 102, 145, 103 of the separatepredictive computational models for consumer segment data, healthcareprovider segment data, retails store segment data and payor segmentdata.

Simultaneously, the brand strategist 4 begins promotional planning at toassure that resources, including dollars, people and processes, aresecured to support the implementation of a promotional campaign designedfrom the output of the predictive model.

If the predictive model at step 51 indicates the combined data arepredictive, the brand strategist 4, through the computer device 3 a,then deploys a promotional campaign that is comprised of consumersegment promotional plans, healthcare provider segment promotionalplans, payor segment promotional plans and retailer segment promotionalplans that are the product of the respective computational models forconsumers, healthcare providers, payors and retailers.

In one embodiment, if the predictive model at step 51 indicates thecombined data are not predictive, the output of the predictive model isused to select a different segment promotional plan modified for aspecified segment within the target consumer group, a specified segmentwithin the target HCP group, a specified segment within the targetretail group and a specified segment within the target payor group forfeeding back iteratively to the computational model of consumer segmentdata, the computational model of healthcare provider segment data andthe computational model of payor segment data and the computationalmodel of retail data for further optimization until a combination ofdata is deemed by the predictive model to be predictive. The computationmodel specifies which segments are not predictive.

In one embodiment, the brand drug value growth and retention engine 18output might call for a promotional campaign the includes consumersegment promotional plans that include push digital advertising of thegrocery store brand of baby aspirin to Facebook, Twitter, YouTube orother social media outlets. The promotional campaign might also includea retail segment promotional plan that calls for providing electroniccoupons to the grocery store chain loyalty card holders distributed tothe card holder's smart phones via SMS, their tablets or on theircomputers as an incentive for them to purchase the grocery store chainbrand of baby aspirin. In the same embodiment the predictive outputmight call for the promotional campaign to include an HCP segmentpromotional plan that includes distributing paper grocery store brandbaby aspirin coupons and samples to certain HCPs for redistribution totheir respective patients. Given that consumers, healthcareprofessionals and payors have changing needs, wants, demands andbehaviors, in this embodiment the application of the brand value growthand retention engine 18 continuously operates and optimizes the grocerystore chain's promotional campaigns for their grocery store brand ofbaby aspirin until the brand strategist 4 determines that he or she nolonger desires to change promotional campaign or no longer desires tocontinue the promotional campaign.

The predictive model determines segment promotional plans for thegrocery store chain's brand of baby aspirin and determines whichpromotional tactic profiles require adjustment to yield a higherresponse rate at a certain investment level over time and therefore apromotional campaign relies on the learning machine in the predictivemodel to reveal which segment promotional plans are optimized or notoptimized.

The brand drug value growth and retention engine 18 is configured togenerate optimal segment promotional plans from the four computationmodels' segment data that are combined and processed through thepredictive model to generate a promotional campaign based on thepredictive model produced.

A promotional campaign for the grocery store chain's brand of babyaspirin comprises a plurality of segment promotional plans. Each segmentpromotional plan is directed to a particular segment of consumers, aparticular segment of healthcare providers, a particular segment ofpayors and a specific segment of retailers with specific tactics towhich a respective segment responds. Different segments of consumers,segments of healthcare providers, segments of retailers and segments ofpayors may have the same or different sets of promotional tactics thatare applied in order to have the predictive model produce an effectivepromotional campaign.

FIG. 6B is a graphical elasticity curve illustrating differentpromotional tactics 69, 70, 71 and 72. The x-axis on the curve denotespromotional pressure 73 and the y-axis on the curve denotes the branddrug volume 74. Each of the promotional tactics 69, 70, 71 and 72 has anoptimal point or region in considering the promotional pressure 73relative to the brand drug volume 74, as indicated by a rectangularpoint 75 depicted on the promotional tactic curve 69, a rectangularpoint 76 depicted on the promotional tactic curve 70, a rectangularpoint 77 on the promotional tactic curve 71, and a rectangular point 78on the promotional tactic curve 72.

FIG. 6C is a flow diagram providing one illustration of promotionalcampaign at time t₀ with multiple promotional tactics that are appliedto multiple segments of consumers. The promotional campaign is directedto a first consumer segment 79, a second consumer segment 80, a thirdconsumer segment 81, and a fourth consumer segment 82. Each of thefirst, second, third, and fourth consumer segments may involve differenttypes of promotional tactics over time as a mechanism to adjust andachieve optimal effectiveness. At time t₀ as shown in FIG. 6C, fourconsumer segments have been identified that are associated with thispromotional campaign; however, the promotional tactics have yet to bedeployed during the first time period 83 and the second time period 84.

Various types of promotional tactics that are deployed for the fourconsumer segments during the first time period 83 are illustrated inFIG. 6D. In this illustration, four exemplary promotional tactics areselected, which are represented by a circular symbol for the firstpromotional tactic, a rectangular symbol for the second promotionaltactic, a triangular symbol for the third promotional tactic, and atrapezoid symbol for the fourth promotional tactic. The symbol sizes ofthe circle, rectangle, triangle and trapezoid represent the amount ofbudget allocated for that particular promotional tactic at that point intime. For the first consumer segment 79 during the first time period 83,the promotional tactics involve the first promotional tactic 85, whichin this example is a coupon/copay card for a portion during the firsttime period 83. The second promotional tactic 86 is then launched for aportion of time during the first time period 83 to the first consumersegment 79, which in this instance is television advertisement of thebrand drug. The promotional tactic then changes to the third type ofpromotional tactic 87, which in this example involves providing samplesof a brand drug to the first consumer segment 79 for a portion of timeduring the first time period 83. After the first three promotionaltactics have been deployed, the fourth promotional tactic 88 supplementsthe previous promotional tactics by working with a payor to advertise tothe first consumer segment 79. Similar types of promotional tactics andsequences are executed for the second consumer segment 80, the thirdconsumer segment 81 and the fourth consumer segment 82 during the firsttime period 83. One significant difference between the promotionaltactics directed to the first, second, third and fourth consumersegments is that the amount of budget allocated for a specificpromotional tactic may differ depending on the suitability for thatparticular consumer segment. For example, the allocated budget fortelevision advertisement 86 for the first consumer segment 79 and thetelevision advertisement 92 for the fourth consumer segment 82 is largerthan the television advertisement budget 89 for the second consumersegment 80. The amount of complementary samples 90 provided by thesecond consumer segment 80 is larger than the amount of samples 87provided to the first consumer segment 79 and the amount of samples 93provided to the fourth consumer segment 82. As for the third consumersegment 81, there is no promotional tactic deployed during the initialportion of the first time period 83, but, instead, a larger televisionadvertisement budget 91 is allocated right after the initial period.

Carefully selected promotional tactics deployed during the second timeperiod 84 for the first, second, third and fourth consumer segments areillustrated in FIG. 6E. In part, based on the effectiveness of theprevious promotional tactics deployed during the first time period 83,adjustments are made to the types of promotional tactics and budgetsizes during the second time period 84. For example, the circle symbolrepresenting a coupon/copay card 94 is expanded for the first consumersegment 79 during the second time period 84 relative to the first timeperiod 83. The rectangular symbol representing television advertisement95 is reduced for the first consumer segment 79 in the second timeperiod 84 compared to the first time period 83. Both the triangle symbolrepresenting samples 96 and the trapezoid symbol representing payorletter 97 are enlarged for the first consumer segment 79 in the secondtime period 84 in relation to the first time period 83. Significantadjustments can be made to the size of the budget allocations as shownin the third consumer segment 81; the triangle symbol representingtelevision advertisement 98 is drastically reduced during the secondtime period 84 relative to the first time period 83, which may signifythat the television advertisement was ineffective as a promotionaltactic to the consumers in the third consumer segment 81. Instead, theamount of samples 99 and payor letter 100 are increased substantiallyduring the second time period 84 relative to the first time period 83,perhaps an indication that the brand drug samples and advertisementthrough payors work quite effective during the first time period 83 forthe third consumer segment 81.

Promotional tactics can vary depending on the advancement of technologyas applied to segments of a population, where popular promotionaltactics may include TV advertisement, Internet advertisement, socialnetworking advertisement, direct mail advertisement, presentations bysales representatives either by telephone, computer or in person, andcopay cards. Social media advertising and mobile adverting have becomeincreasingly attractive as promotional tactics due to the large numberof users. Feedbacks from these social media companies and mobile devicescan serve as the basis to determine the effectiveness of a particularpromotional tactic. For example, a predictive model can derive in partby “like button” on social media companies like Yelp, Facebook, andLinkedIn, or compilation of unstructured data from Twitter, Yelp,WhatsApp, Line, WeChat as well as other social media sources on consumerusers' comments about brand drugs. The unstructured data from socialmedia that have been analyzed can provide meaningful feedback into thepredictive model for tracking chatters on social media or mobile devicesto drug consumption behavior. Our data platform can help to uncover theonline behaviors of consumers, map the promotional tactics thatinfluence their behavior and provide insights into their engagement andbuying patterns. In addition, the analysis of the unstructured data canfurther contribute in personalizing a promotional messaging, improvingtargeting and even designing new tactics to increase the effectivenessof the promotional campaigns.

FIG. 8 is a graphical curve illustrating the volume of a brand drugproduced by a conventional approach and compares it to the volume of thesame brand drug using the promotional campaign generated by the brandvalue growth and retention engine 18. The curve has an x-axis 104representing time, and a y-axis 105 denoting the percentage of branddrug volume. A dash line 108 in the center denotes when the brand drugloses exclusivity of patent. A first curve 107 represents the brandvolume of the conventional approach, and a second curve 106 representsthe brand volume with the impact of the promotional campaign executed bythe brand drug value growth and retention engine 18. Prior to the lossof exclusivity, there in a first area 109 between the two curves 106 and107, which shows the incremental brand drug volume produced by thepromotional campaign as applied to the brand drug. After the loss ofexclusivity around the time of dash line 108, there is a second area 110between the two curves 106 and 107, which also shows the incrementalvalue produced by the promotional campaign as applied to the brand drug.The comparison of the two exemplary curves, 106, 107, illustrates inparticular that the first curve has data points without a promotionalcampaign, leading to loss in value both before and after LOE.

FIG. 9A is a flow diagram illustrating the communications between thebrand strategist dashboard 25, the drug manufacture dashboard 25 and thepayor dashboard 25. The brand drug value growth and retention engine 18is configured to supply data periodically, such as on a daily basis, tothe brand strategist dashboard 25. The brand strategist dashboard 25,through the brand drug value growth and retention engine 18, pushesfiltered and customized information to customize the manufacturedashboard 25 and the payor dashboard 25. The manufacture dashboard 25furnishes information to and receives information from a C-suit manager111, a brand management manager 112, a sales/payor operations manager113, a sales force manager 114 and a finance manager 115. The payordashboard 25 furnishes information to and receives information from thePBMs 8, the commercial manager 116, a compliance manager 117, a copayutilization manager 118, a patient trends manager 119 and a financemanager 120. The communications between the brand strategist dashboard25, the drug manufacture dashboard 25 and the payor dashboard 25 ensuresthat checks and balances are maintained.

FIG. 9B is a pictorial diagram illustrating one embodiment of the brandvalue growth and retention dashboard (also referred to as “brandstrategist dashboard”) 25. The brand strategist dashboard 25 displaysdata received from the brand drug value growth and retention engine 18for the brand strategist 4 to manage various components of a promotionalcampaign. The brand strategist dashboard 25 in FIG. 9B illustrates anexemplary embodiment, and other variations and modifications of thebrand strategist dashboard 25 can be implemented without departing fromthe spirit of the present invention. The brand strategist dashboard 25is divided into several sections, including a brand drug section 121, apromotional campaign 65, a promotional tactics 122, a consumer segmentscomputational model 123, a healthcare provider segments computationalmodel 124, a retail store segments model 125, a payor segmentcomputational model 126, a particular brand drugs X incremental sales127, a DTC dollars spent on a particular brand drug X 128, incrementalrevenue generated through incremental brand drug X volume by promotionaltactics 129, and an alternate promotional campaign 130. With the brandstrategist dashboard 25, (clarify the dashboard) the consumer segmentscomputational model dashboard section 123 receives data from andtransmits data to step 47 which executes a computational model onconsumer segments, a healthcare provider segments computational modeldashboard section 124 receives data from and transmit data to step 48which conducts a computational model on healthcare provider segments,the dashboard portion of a retail store segments model section 125receives data from and transmit data to step 49 which executes acomputational model on retail store segments, a payor segmentcomputational model 126 receives data from and transmit data to step 50which conducts a computational model simulation on payor segments. Thefinancial model simulator module 26 is configured to supply thepromotional campaign information to the promotional tactics 122. Theconsumer segments module 28 a is configured to supply data to theconsumer segments model section 123. The healthcare provider segmentsmodule 28 b is configured to supply data to the HCP segments modelsection 124. The manufacturer PBM/payor strategy module 31 and themanufacturer PBM/payor execution module 32 are configured to supply tothe PBM/payor computational model 126 and the retailer segments module28 c is configured to supply data to the retail store segments modelsection 125. The manufacturer PBM/payor strategy module 31 and themanufacturer PBM/payor execution module 32 are configured to supply tothe PBM/payor computational model 126.

FIGS. 9C-9I are exemplary graphs that may be displayed on differentsections of the brand strategist dashboard. FIG. 9C is a bar graphillustrating a particular brand drug Incremental Sales: budget,year-to-date (YTD) target, and YTD actual. FIG. 9D is a graph showingDTC dollars spent on a particular brand drug. A bar graph in FIG. 9Edepicts dollars generated through incremental brand drug volume forspecified months during a particular year. FIG. 9F shows PBM data for aparticular brand drug copay cards redeemed by month. A sample of thecopay cards distributed by a particular month for the brand drug isillustrated in FIG. 9G. FIG. 9H illustrates the amount of sales of aparticular brand drug for a particular month during the current yearversus the particular month from last year. A sampling of DTC exposuresfor a particular brand drug is depicted in FIG. 9I.

FIG. 10 is a block diagram illustrating an example of a computer device,as shown in 3 a, 3 b, 3 c, 3 d, 3 e, 3 f, 3 g, 3 h, 3 i and 3 j, onwhich computer-executable instructions to perform the methodologiesdiscussed herein may be installed and run. As alluded to above, thevarious computer-based devices discussed in connection with the presentinvention may share similar attributes. Each of the computer devices in3 a, 3 b, 3 c, 3 d, 3 e, 3 f, 3 g, 3 h, 3 i and 3 j is capable ofexecuting a set of instructions to cause the computer device to performany one or more of the methodologies discussed herein. The computerdevices may represent any or all of the 3 a, 3 b, 3 c, 3 d, 3 e, 3 f, 3g, 3 h, 3 i, and 3 j server 10, or any network intermediary devices.Further, while only a single machine is illustrated, the term “machine”shall also be taken to include any collection of machines thatindividually or jointly execute a set (or multiple sets) of instructionsto perform any one or more of the methodologies discussed herein. Theexemplary computer system 131 includes a processor 132 (e.g., a centralprocessing unit (CPU), a graphics processing unit (GPU), or both), amain memory 133 and a static memory 134, which communicate with eachother via a bus 135 and project onto a display 136. The computer system131 may further include a video display unit 137 (e.g., a liquid crystaldisplay (LCD)). The computer system 131 also includes an alphanumericinput device 138 (e.g., a keyboard), a cursor control device 139 (e.g.,a mouse), a disk drive unit 140, a signal generation device 141 (e.g., aspeaker), and a network interface device 144.

The disk drive unit 140 includes a machine-readable medium 142 on whichis stored one or more sets of instructions (e.g., software 143)embodying any one or more of the methodologies or functions describedherein. The software 143 may also reside, completely or at leastpartially, within the main memory 133 and/or within the processor 132during execution thereof, the computer system 131, the main memory 133,and the instruction-storing portions of the processor 132 alsoconstituting machine-readable media. The software 143 may further betransmitted or received over a network 5 via the network interfacedevice 144.

While the machine-readable medium 142 is shown in an exemplaryembodiment to be a single medium, the term “machine-readable medium”should be taken to include a single medium or multiple media (e.g., acentralized or distributed database, and/or associated caches andservers) that store one or more sets of instructions. The term“machine-readable medium” shall also be taken to include any tangiblemedium that is capable of storing a set of instructions for execution bythe machine and that cause the machine to perform any one or more of themethodologies of the present invention. The term “machine-readablemedium” shall accordingly be taken to include, but not be limited to,solid-state memories, and optical and magnetic media.

The present invention has been described in particular detail withrespect to possible embodiments. Those skilled in the art willappreciate that the invention may be practiced in other embodiments. Theparticular naming of the components, capitalization of terms, theattributes, data structures, or any other programming or structuralaspect is not mandatory or significant, and the mechanisms thatimplement the invention or its features may have different names,formats or protocols. The system may be implemented via a combination ofhardware and software, as described, or entirely in hardware elements,or entirely in software elements. The particular division offunctionality between the various system components described herein ismerely exemplary and not mandatory; functions performed by a singlesystem component may instead be performed by multiple components, andfunctions performed by multiple components may instead be performed by asingle component.

In various embodiments, the present invention can be implemented as asystem or a method for performing the above-described techniques, eithersingly or in any combination. In another embodiment, the presentinvention can be implemented as a computer program product comprising acomputer-readable storage medium and computer program code, encoded onthe medium, for causing a processor in a computing device or otherelectronic device to perform the above-described techniques.

As used herein, any reference to “one embodiment” or to “an embodiment”means that a particular feature, structure or characteristic describedin connection with the embodiments is included in at least oneembodiment of the invention. The appearances of the phrase “in oneembodiment” in various places in the specification are not necessarilyall referring to the same embodiment.

Some portions of the above are presented in terms of algorithms andsymbolic representations of operations on data bits within a computermemory. These algorithmic descriptions and representations are the meansused by those skilled in the data processing arts to most effectivelyconvey the substance of their work to others skilled in the art. Analgorithm is generally perceived to be a self-consistent sequence ofsteps (instructions) leading to a desired result. The steps are thoserequiring physical manipulations of physical quantities. Usually, thoughnot necessarily, these quantities take the form of electrical, magneticor optical signals capable of being stored, transferred, combined,compared, transformed, and otherwise manipulated. It is convenient attimes, principally for reasons of common usage, to refer to thesesignals as bits, values, elements, symbols, characters, terms, numbers,or the like. Furthermore, it is also convenient at times to refer tocertain arrangements of steps requiring physical manipulations ofphysical quantities as modules or code devices, without loss ofgenerality.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise as apparent from the following discussion,it is appreciated that, throughout the description, discussionsutilizing terms such as “processing” or “computing” or “calculating” or“displaying” or “determining” or the like refer to the action andprocesses of a computer system, or similar electronic computing moduleand/or device, that manipulates and transforms data represented asphysical (electronic) quantities within the computer system memories orregisters or other such information storage, transmission or displaydevices.

Certain aspects of the present invention include process steps andinstructions described herein in the form of an algorithm. It should benoted that the process steps and instructions of the present inventioncould be embodied in software, firmware and/or hardware, and, whenembodied in software, can be downloaded to reside on and be operatedfrom different platforms used by a variety of operating systems.

The present invention also relates to an apparatus for performing theoperations herein. This apparatus may be specially constructed for therequired purposes, or it may comprise a general-purpose computerselectively activated or reconfigured by a computer program stored inthe computer. Such a computer program may be stored in a computerreadable storage medium, such as, but not limited to, any type of diskincluding floppy disks, optical disks, CD-ROMs, magnetic-optical disks,read-only memories (ROMs), random access memories (RAMs), EPROMs,EEPROMs, magnetic or optical cards, application specific integratedcircuits (ASICs), or any type of media suitable for storing electronicinstructions, and each coupled to a computer system bus. Furthermore,the computers and/or other electronic devices referred to in thespecification may include a single processor or may be architecturesemploying multiple processor designs for increased computing capability.

The algorithms and displays presented herein are not inherently relatedto any particular computer, virtualized system, or other apparatus.Various general-purpose systems may also be used with programs inaccordance with the teachings herein, or it may prove convenient toconstruct more specialized apparatus to perform the required methodsteps. The required structure for a variety of these systems will beapparent from the description provided herein. In addition, the presentinvention is not described with reference to any particular programminglanguage. It will be appreciated that a variety of programming languagesmay be used to implement the teachings of the present invention asdescribed herein, and any references above to specific languages areprovided for disclosure of enablement and best mode of the presentinvention.

In various embodiments, the present invention can be implemented assoftware, hardware, and/or other elements for controlling a computersystem, computing device, or other electronic device, or any combinationor plurality thereof. Such an electronic device can include, forexample, a processor, an input device (such as a keyboard, mouse,touchpad, trackpad, joystick, trackball, microphone, and/or anycombination thereof), an output device (such as a screen, speaker,and/or the like), memory, long-term storage (such as magnetic storage,optical storage, and/or the like), and/or network connectivity,according to techniques that are well known in the art. Such anelectronic device may be portable or non-portable. Examples ofelectronic devices that may be used for implementing the inventioninclude mobile phones, personal digital assistants, smartphones, kiosks,desktop computers, laptop computers, tablets, wearable devices, wearablesensors, consumer electronic devices, televisions, set-top boxes or thelike. An electronic device for implementing the present invention mayuse an operating system such as, for example, iOS available from AppleInc. of Cupertino, Calif., Android available from Google Inc. ofMountain View, Calif., Microsoft Windows 7 available from MicrosoftCorporation of Redmond, Wash., webOS available from Palm, Inc. ofSunnyvale, Calif., or any other operating system that is adapted for useon the device. In some embodiments, the electronic device forimplementing the present invention includes functionality forcommunication over one or more networks, including for example acellular telephone network, wireless network, and/or computer networksuch as the Internet.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. It should be understood thatthese terms are not intended as synonyms for each other. For example,some embodiments may be described using the term “connected” to indicatethat two or more elements are in direct physical or electrical contactwith each other. In another example, some embodiments may be describedusing the term “coupled” to indicate that two or more elements are indirect physical or electrical contact. The term “coupled,” however, mayalso mean that two or more elements are not in direct contact with eachother, but yet still co-operate or interact with each other. Theembodiments are not limited in this context.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof are intendedto cover a non-exclusive inclusion. For example, a process, method,article or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

The terms “a” or “an,” as used herein, are defined as one or more thanone. The term “plurality,” as used herein, is defined as two or morethan two. The term “another,” as used herein, is defined as at least asecond or more.

The term “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, “A or B” means“A, B, or both,” unless expressly indicated otherwise or indicatedotherwise by context. Moreover, the term “and” is both joint andseveral, unless expressly indicated otherwise or indicated otherwise bycontext. Therefore, “A and B” means “A and B, jointly or severally,”unless expressly indicated otherwise or indicated otherwise by context.

An ordinary artisan should require no additional explanation indeveloping the methods and systems described herein but may neverthelessfind some possibly helpful guidance in the preparation of these methodsand systems by examining standard reference works in the relevant art.

While the invention has been described with respect to a limited numberof embodiments, those skilled in the art, having benefit of the abovedescription, will appreciate that other embodiments may be devised whichdo not depart from the scope of the present invention as describedherein. It should be noted that the language used in the specificationhas been principally selected for readability and instructionalpurposes, and may not have been selected to delineate or circumscribethe inventive subject matter. The terms used should not be construed tolimit the invention to the specific embodiments disclosed in thespecification and the claims but should be construed to include allmethods and systems that operate under the claims set forth hereinbelow. Accordingly, the invention is not limited by the disclosure, butinstead its scope is to be determined entirely by the following claims.

What is claimed and desired to be secured by Letters Patent of theUnited States is:
 1. A computer-implemented method for generating areal-time computational predictive model in a healthcare industry, thecomputer-implemented method comprising: running, by a processor, a cloudoperating system and an engine, the engine runs on the cloud operatingsystem; executing, by the processor, to generate a first computermodeling on an amount of consumer segment data retrieved via the enginefrom a first virtual database to determine a first output data, thefirst output data representing a substantially optimal brand drugpromotional mix from a first combination of promotional electronicmethodologies; executing, by the processor, to generate a secondcomputer modeling on an amount of healthcare provider segment dataretrieved via the engine from a second virtual database to determine asecond output data, the second output data representing a substantiallyoptimal brand drug promotional mix from a second combination ofpromotional electronic methodologies; executing, by the processor, togenerate a third computer modeling on an amount of retail store segmentdata retrieved via the engine from a third virtual database to determinea third output data, the third output data representing a substantiallyoptimal product mix; executing, by the processor, to generate a fourthcomputer modeling on an amount of payor segment data retrieved via theengine from a fourth virtual database to determine a fourth output data,the fourth output data representing a substantially optimal contractingstrategy, the first virtual database, the second virtual database, andthe fourth virtual database define a virtual storage, the engine is incommunication with the virtual storage, the third virtual database andthe engine are external to the virtual storage; generating, by theprocessor, a real-time predictive computational model from the first,second, third, and fourth computer modelings for a brand drug to retainthe brand drug value prior to the loss of patent exclusivity, thereal-time computational predictive model of the first, second, third andfourth computer modelings including the consumer segment data,healthcare provider segment data, retail store segment data and thepayor segment data, based on a combination of outputs from the first,second, third and fourth computer modelings; taking, by the processor,an action based on the real-time predictive computational model, theaction is requested via an input device of a workstation incommunication with the processor and external to the virtual storage andthe engine, wherein the predictive computational model is computed fromthe following equation:${SPP} = {\sum\limits_{{i = 1},m}\; {{\alpha \left( t_{i} \right)}\beta_{i}{T_{i}\left( {F_{i},S_{j}} \right)}}}$wherein the predictive computational model comprises data associatedwith a plurality of segment promotional plans (SPP), the symbol α(t_(i))representing temporal preferences including order and weight, and theterm β_(i),*T_(i)(F_(i), S_(j)) denoting one or more tactic profiles,coefficient β_(i) denoting weighted factors applying respectively eachcorresponding tactic profile, the frequency F_(i) applying to thecorresponding promotional tactic T_(i) up to the m^(th) tactic, suchthat the symbol m represents the total number of tactics.
 2. The methodof claim 1, further comprising: generating a first predictive elementfrom the first computational model on the consumer segment data;generating a second predictive element from the second computationalmodel on healthcare provider segment data; and generating a thirdpredictive element from the third computational model on retail storesegment data; generating a fourth predictive element from the fourthcomputational model on payor segment data; wherein the predictivecomputational model is generated based on a quadripartite combination ofthe first predictive element, the second predictive element, the thirdpredictive element and the fourth predictive element.
 3. The method ofclaim 2, wherein each of the first, second, third and fourth predictiveelements partially affects the predictive model in generating thereal-time predictive computational model.
 4. The method of claim 1,wherein the a real-time predictive computational model comprises aplurality of segment promotional plans, each promotional plan includingone or more tactic profiles, each tactic profile being selected when aconsumer segment in the consumer segment data responds to a particularpromotional tactic.
 5. The method of claim 1, wherein the a real-timepredictive computational model comprises a plurality of segmentpromotional plans, each promotional plan including one or more tacticprofiles, each tactic profile being created and selected when a consumersegment in the consumer segment data responds to a particularpromotional tactic.
 6. The method of claim 1, wherein the predictivecomputational model is adaptive to a change in a market response, themarket response being affected by the first, second and thirdcomputational models.
 7. The method of claim 6, wherein the predictivecomputational model is adapted via the application of a learning machinethat estimates parameters thereby generating a transformed predictivemodel.
 8. The method of claim 6, wherein the predictive computationalmodel is adapted via the application of a learning machine that modifiesexisting parameters thereby generating a transformed predictive model.9. The method in claim 1, wherein the predictive computational modelcombines information from computational models in a linear manner,wherein the combined information includes at least two of the consumersegment data, healthcare provider segment data, retail sales data, andthe payor segment data.
 10. The method in claim 9, wherein the combinedinformation in the predictive computational model provides explicitweights to one or more components in the combined information.
 11. Themethod in claim 1 wherein the real-time predictive computational modelis a weighted combination of the data associated with segmentpromotional plans (SPP's).
 12. The method of claim 1, wherein thereal-time predictive computational model comprises explicit interactionterms among the actual or planned segment promotional plans as well asindividual segment promotional plans (SPPs).
 13. The method of claim 2,wherein the initial predictive computational model is modified to takeinto accounts interactions among the three predictive elements.
 14. Themethod of claim 1, further comprising selecting data associated with adifferent segment promotional plan modifying the information in thepredictive computational model for a particular consumer segment withinthe consumer segments if the predictive model does not meet apredetermined substantially optimal threshold.
 15. The method of claim1, further comprising training a learning machine by invoking a machinelearning method to estimate and attempt to optimize parameters forprediction of one or more computational models sourced from theexecuting step of the first computational model, the executing step ofthe second computational model, and executing step of the thirdcomputational model.
 16. The method of claim 4, wherein the machinelearning method comprises an active or proactive learning in which a newprior predictive computational model attempts to jointly optimize bothnew knowledge gained about the effectiveness of the new prior predictivecomputational model and the immediate impact of the selected priorpredictive computational model and data associated with promotionaltactics.
 17. The method of claim 15, wherein the learning step compriseslearning from data sourced from the current campaign, data sourced fromat least one prior predictive computational model, and data sourced fromexternal market reception to the current predictive computational model,to generalize learning from the collection of data.
 18. The method ofclaim 15, wherein the machine learning method comprises an active orproactive learning in which a new tactic may attempt to jointly optimizeboth new knowledge gained about the effectiveness of the new tactics andthe immediate impact of the selected campaigns and tactics.
 19. Themethod of claim 15, further comprising re-estimating one or morecomputational models if not all results in the step of estimating andattempting to optimize parameters for prediction of one or moreplurality of computational models are positive.
 20. The method in claim15, wherein the machine learning method suggests multiple potential butmutually exclusive improvements where one improvement is not positiveand re-estimating the others from the feedback of the first testedimprovement.
 21. A computer-implemented method for generating areal-time predictive computational model in a healthcare industry, thecomputer-implemented method comprising: running, via a server, a cloudoperating system and an engine, the engine runs on the cloud operatingsystem; executing, via the server, a first computational model on a setof consumer segment data to determine a first substantially optimalbrand drug promotional mix, the set of consumer segment data isretrieved via the engine from a first virtual database; executing, viathe server, a second computational model on a set of healthcare providersegment data to determine a second substantially optimal brand drugpromotional mix, the set of healthcare provider segment data isretrieved via the engine from a second virtual database; executing, viathe server, a third computational model on a set of retail store segmentdata to determine a substantially optimal product mix, the set of retailstore segment data is retrieved via the engine from a third virtualdatabase; executing, via the server, a fourth computational model on aset of payor segment data to determine a substantially optimalcontracting strategy for a brand drug, set of payor segment data isretrieved via the engine from a fourth virtual database, the firstvirtual database, the second virtual database, and the fourth virtualdatabase define a virtual storage, the engine is in communication withthe virtual storage, the third virtual database and the engine areexternal to the virtual storage; generating, via the server, apredictive computational model of the consumer segment data, healthcareprovider segment data, retail store segment data and the payor segmentdata, based on a combination of outputs from the first, second, thirdand fourth computational models to retain the brand drug value prior tothe loss of the patent exclusivity; and taking, via the server, anaction based on the predictive computational model, the action isrequested via an input device of a client in communication with theserver and external to the virtual storage and the engine, wherein thepredictive computational model is computed from the following equation:${SPP} = {\sum\limits_{{i = 1},m}\; {{\alpha \left( t_{i} \right)}\beta_{i}{T_{i}\left( {F_{i},S_{j}} \right)}}}$wherein the predictive computational model comprises data associated aplurality of segment promotional plans (SPP), the symbol α(t_(i))representing temporal preferences including order and weight, and theterm β_(i),*T_(i)(F_(i), S_(j)) denoting one or more tactic profiles,coefficient β_(i), denoting weighted factors applying respectively eachcorresponding tactic profile, the frequency F_(i) applying to thecorresponding promotional tactic T_(i) up to the m^(th) tactic, suchthat the symbol m represents the total number of tactics, the termα(t)'s denoting any ordering or parallelizing temporal function.
 22. Themethod of claim 21, wherein the real-time predictive computational modelis represented by the following equation:${PC} = {\sum\limits_{{i = 1},N}\; {{\alpha \left( t_{i} \right)}{SPP}_{i}}}$23. The method of claim 22, wherein the explicit interaction terms arebinary, comprising a planned or actual SPP interacting with a secondplanned or actual SPP.
 24. A computer-implemented method for generatinga real-time computational predictive model in a healthcare industry, thecomputer-implemented method comprising: running, via a server, a cloudoperating system and an engine, the engine runs on the cloud operatingsystem; executing, via the server, a first computational model on a setof consumer segment data to determine a first substantially optimalbrand drug promotional mix for consumers, the set of consumer segmentdata is retrieved via the engine from a first virtual database;executing, via the server, a second computational model on a set ofhealthcare provider segment data to determine a second substantiallyoptimal brand drug promotional mix for healthcare providers, the set ofhealthcare provider segment data is retrieved via the engine from asecond virtual database; executing, via the server, a thirdcomputational model on a set of retail store segment data to determine asubstantially optimal product mix, the set of retail store segment datais retrieved via the engine from a third virtual database; executing,via the server, a fourth computational model on a set of payor segmentdata to determine a substantially optimal contracting strategy for abrand drug, the set of payor segment data is retrieved via the enginefrom a fourth virtual database, the first virtual database, the secondvirtual database, and the fourth virtual database define a virtualstorage, the engine is in communication with the virtual storage, thethird virtual database and the engine are external to the virtualstorage; generating, via the server, a predictive computational model ofthe consumer segment data, healthcare provider segment data, retailstore segment data and the payor segment data, based on a combination ofoutputs from the first, second, third and fourth computational models toretain the brand drug value prior to the loss of patent exclusivity; andtaking, via the server, an action based on the predictive computationalmodel, the action is requested via an input device of a client incommunication with the server and external to the virtual storage andthe engine, wherein the predictive computational model (PC) isrepresented by the following equation, where j=1, M ranges over dataassociated with all segment promotional plans as part of the predictivecomputational model, and the function G_(SPP) (SPP_(i), SPP_(j))computes potential or actual interactions among the data associated witha plurality of segment promotional plans contained in the predictivecomputational model:${PC} = {\sum\limits_{{j = 1},M}\; {\sum\limits_{{i = 1},N}\; {{\alpha \left( t_{i} \right)}\left\lbrack {{SPP}_{i} + {G_{spp}\left( {{SPP}_{i},{SPP}_{j}} \right)}} \right\rbrack}}}$25. The method of claim 24, wherein the interactions among thepredictive elements are binary comprising a first predictive elementinteraction with a second predictive element.
 26. A computer-implementedmethod for generating a real-time computational predictive model in ahealthcare industry, the computer-implemented method comprising:running, via a server, a cloud operating system and an engine, theengine runs on the cloud operating system; executing, via the server, afirst computational model on a set of consumer segment data to determinea first substantially optimal brand drug promotional mix for consumers,the set of consumer segment data is retrieved via the engine from afirst virtual database; executing, via the server, a secondcomputational model on a set of healthcare provider segment data todetermine a second substantially optimal brand drug promotional mix forhealthcare providers, the set of healthcare provider segment data isretrieved via the engine from a second virtual database; executing, viathe server, a third computational model on a set of retail store segmentdata to determine a substantially optimal product mix for retail storesthat sell the brand drug, the set of retail store segment data isretrieved via the engine from a third virtual database; executing, viathe server, a fourth computational model on a set of payor segment datato determine a substantially optimal contracting strategy for a branddrug, the set of payor segment data is retrieved via the engine from afourth virtual database, the first virtual database, the second virtualdatabase, and the fourth virtual database define a virtual storage, theengine is in communication with the virtual storage, the third virtualdatabase and the engine are external to the virtual storage; generating,via the server, a predictive computational of the consumer segment data,healthcare provider segment data, retail store segment data and thepayor segment data, based on a combination of outputs from the first,second, third and fourth computational models to retain the brand drugvalue prior to the loss of patent exclusivity; and taking, via theserver, an action based on the predictive computational model, theaction is requested via an input device of a client in communicationwith the server and external to the virtual storage and the engine,wherein the interaction among the three predictive elements (PEs) isgoverned by the following equation, where the function GPE computespotential or actual interactions among the three predictive elements:${PC}_{modified} = {{PC}_{initial}{\sum\limits_{{j = 1},4}\; {\sum\limits_{{i = 1},A,{i \neq j}}\; \left\lbrack {1 + {G_{pe}\left( {{PE}_{i},{PE}_{j}} \right)}} \right\rbrack}}}$27. The method of claim 26, further comprising determining and affectingthe parameters in the predictive computational model and providingfeedback to a learning machine to re-estimate parameters and revisecorresponding predictions from one or more of the three predictiveelements.